Methods and Systems for an Automated Design, Fulfillment, Deployment and Operation Platform for Lighting Installations

ABSTRACT

A platform for design of a lighting installation generally includes an automated search engine for retrieving and storing a plurality of lighting objects in a lighting object library and a lighting design environment providing a visual representation of a lighting space containing lighting space objects and lighting objects. The visual representation is based on properties of the lighting space objects and lighting objects obtained from the lighting object library. A plurality of aesthetic filters is configured to permit a designer in a design environment to adjust parameters of the plurality of lighting objects handled in the design environment to provide a desired collective lighting effect using the plurality of lighting objects.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/664,546 filed on Oct. 25, 2019 and entitled Methods and Systems forAn Automated Design, Fulfillment, Deployment and Operation Platform forLighting Installations, which is a continuation of U.S. patentapplication Ser. No. 16/601,711 filed on Oct. 15, 2019 and entitledMethods and Systems for An Automated Design, Fulfillment, Deployment andOperation Platform for Lighting Installations, which is a continuationof Patent Cooperation Treaty (PCT) International Patent ApplicationSerial No. PCT/US2018/029380 filed on Apr. 25, 2018 and entitled Methodsand Systems for An Automated Design, Fulfillment, Deployment andOperation Platform for Lighting Installations, which claims the benefitof U.S. Provisional Patent Application Ser. No. 62/491,137 filed on Apr.27, 2017 and entitled Methods and Systems for An Automated Design,Fulfillment, Deployment and Operation Platform for LightingInstallations, and which claims the benefit of U.S. Provisional PatentApplication Ser. No. 62/562,714 filed on Sep. 25, 2017 and entitledMethods and Systems for An Automated Design, Fulfillment, Deployment andOperation Platform for Lighting Installations, each of the foregoingapplications being commonly-owned and all of which hereby areincorporated by reference as if fully set forth herein in theirentirety.

BACKGROUND

This disclosure relates to the field of lighting, more particularly toan automated platform, with automation and machine learning features,for design, fulfillment, deployment, and operation of a lightinginstallation.

Typically, a critical component for the success of an architectural orinterior design project is lighting design. Lighting elements may play anumber of important functional roles in a space, including rendering (orfailing to render) colors of walls, fabrics, furniture and otherelements in a space that is illuminated, engendering emotional reactionsof individuals in the space, causing biological effects on individualsin the space, and acting as fixtures that harmonize with other fixtures(or fail to do so) to establish an aesthetic design, among others.However, current workflows for lighting design can be significantlyflawed. The market for lighting is highly fragmented, with thousands offixtures and illumination sources being provided by many differentsuppliers. In many cases, information about lighting products isunavailable, limited, or inaccurate. For example, online searches, tothe extent that they provide any technical information about a lightingfixture, typically provide only rudimentary information aboutcharacteristics of a light (such as via IES files that provide somebasic information about the number of lumens a lighting product providesover a far field area). As a result, most designers of lightinginstallations need to use physical samples of a lighting product todetermine its properties and evaluate its suitability for a giveninstallation. Lighting fixtures tend to be collected in “sample closets”maintained by designers, sales representatives, and distributors, towhich designers travel in order to characterize a lighting fixture inperson. This process can take days or weeks for a large lightinginstallation, and even in that case often produces suboptimal results,because comparisons between products are unscientific, and designersoften are not even aware of lighting products that may better suit theneeds of a given installation. A need exists for fundamentally differentlighting design workflows.

Meanwhile, lighting products and the environments in which they arelocated are increasingly intelligent. Many lighting fixtures areconfigured to operate as part of the Internet of Things (IoT), so thatthey can be connected to networks for communication with remote systems(such as in the cloud), can be controlled remotely, can communicate witheach other and with other IoT devices in the same spaces (such asbeacons and thermostats), and can operate with some degree of autonomy.However, due to the fragmented nature of the lighting market and thelack of expertise on the part of most designers, most lightinginstallations do little to take advantage of this increasedintelligence. A need exists for methods and systems that enable lightingdesigners and occupants or owners (the term “occupant” or “owner”referring, except where context indicates otherwise, to encompasscustomers and clients of designers, building owners, tenants, workersand other parties occupying the spaces in which lighting systems areinstalled) to design, acquire, install, operate and maintain lightinginstallations that use intelligent features much more effectively tosatisfy a wide variety of requirements.

SUMMARY

Systems and methods are described herein that use a variety of novelinformation technology components to enable coordination of afundamentally different workflow for design, acquisition, installation,operation and maintenance of a lighting installation that leverages theintelligence of lighting fixtures, including lighting fixtures that usenovel control capabilities that are coordinated with other systemcomponents as described herein. References to the platform are intendedto encompass, except where the context indicates otherwise, the variousmethods, systems, components, modules, fixtures, data structures,workflows, and other elements that are coordinated, in variousembodiments, to enable the workflow.

In embodiments, a platform for the design of a lighting installationthat includes an automated search engine for retrieving and storing aplurality of lighting objects in a lighting object library. The platformincludes a lighting design environment providing a visual representationof a lighting space containing lighting space objects and lightingobjects. The visual representation is based on properties of thelighting space objects and lighting objects obtained from the lightingobject library. The platform also includes a plurality of aestheticfilters configured to permit a designer in the design environment toadjust parameters of a plurality of lighting objects handled in thedesign environment to provide a desired collective lighting effect usingthe plurality of lighting objects.

In embodiments, methods and systems for generating a data structure thatcharacterizes a near field illumination pattern generated by a lightsource includes disposing a surface at at least one distance of aplurality of distances in proximity to a light source so that a firstside of the surface is illuminated directly by the light source,capturing, with at least a two-dimensional image sensor, a plurality ofluminance values from at least one the side of the surface; andgenerating a data structure characterizing an illumination fieldgenerated by the light source including a pattern of luminance values onat least the first side of the surface for the at least one distanceamong the plurality of distances.

In embodiments, the data structure further characterizes a positioningof the two-dimensional image sensor. In embodiments, the methods includestoring an image captured by the image sensor; and repeating thedisposing of the surface, the capturing of the plurality of luminancevalues, and the generating of the data structure for a plurality ofincremental distances between the light source and the surface. Inembodiments, the repeating causes storing a plurality ofincremental-distance differentiated images of the luminance of the lightsource.

In embodiments, the methods include storing the images for a pluralityof light sources in a library that enables a user to search the libraryto identify a light source having a desired pattern of illumination. Inembodiments, the methods include a pattern matching system for matchingat least one image in the library to a specified pattern of illuminationfor a space and for facilitating identification of at least one lightsource that provides the specified pattern. In embodiments, the methodincludes a pattern matching system for matching a specified pattern ofillumination for a space with portions of images in the library. Inembodiments, the portions of images include an off-axis slice through aportion of the plurality of the incremental-distance differentiatedimages. In embodiments, the method includes a user interface configuredto permit a user to at least one of specify and select a pattern ofillumination for a space. In embodiments, the pattern of illumination isautomatically provided as an input to the pattern matching system. Inembodiments, the plurality of luminance values is stored for the side ofthe surface that is directly illuminated by the light source. Inembodiments, the surface is translucent and wherein the plurality ofluminance values is stored for a side of the surface that is oppositethe first side that is directly illuminated by the light source. Inembodiments, the method includes applying indirect measurement softwareto generate an area-source model of the light source. In embodiments,disposing a surface includes dynamically disposing the surface with avariable distance device that facilitates varying a distance between thelight source and the surface.

In embodiments, the method includes generating a 3D volumetric luminancemodel from the two-dimensional measurements by arranging the pluralityof incremental-distance differentiated images into a three-dimensionalshape, in embodiments, each of the images represents a slice of thethree-dimensional shape. In embodiments, the method includes convertingwith a processor at least one of the plurality of luminance values to ameasure of luminous flux including values for θ and φ. In embodiments,near field illumination characterization includes luminous flux along θand φ directions for each of a plurality of points on a surface of alight source, and x and y image sensor location data of each of theplurality of luminance values.

In embodiments, the x and y image sensor location data maps tocorresponding x and y location data of the light source. In embodiments,the near field illumination characterization is dependent on at leastone of: (i) distance between the light source and the surface, (ii) anangle between a line projected from the light source and a position onthe surface associated with one of the plurality of luminance values anda normal to the surface, (iii) an optical property of the surface, and(iv) the captured luminance value associated with the position of thesurface. In embodiments, methods and systems for characterizing the nearfield illumination pattern generated by a light source include apositioning slide for holding a screen and moving the screen among aplurality of distances from the light source; at least a two-dimensionalimage sensor for capturing luminance values from at least one side ofthe screen when the screen is illuminated by the light source; and astorage system for storing a plurality of data structures, each datastructure representing the luminance values captured by the at leasttwo-dimensional image sensor at a given distance of the positioningslide for a given light source.

In embodiments, the plurality of data structures is stored in asearchable library. In embodiments, the method includes a user interfaceconfigured to permit a user to search for a light source having adesired pattern of luminance values. In embodiments, methods and systemsof near-field illumination pattern matching, include capturing aplurality of two dimensional images of an illumination effect in anenvironment illuminated by a light source; storing a portion of theplurality of images in a digital data structure that facilitatesdistinguishing among the stored data values in each of the plurality ofimages by a two-dimensional location in an image of the plurality ofimages and an effective distance of the image from the light source;detecting a specified pattern of illumination of the environment in thedigital data structure. In embodiments, the pattern includes a pluralityof data values identified by a two-dimensional location value and lightsource distance value.

In embodiments, at least two of the data values in the specified patternare located at different light source distance values. In embodiments,the light source distance value varies across a portion of the specifiedpattern of illumination. In embodiments, the plurality oftwo-dimensional images is non-co-planar. In embodiments, the images inthe plurality of two-dimensional images are substantially parallel. Inembodiments, the images in the plurality of two-dimensional images aresubstantially parallel. In embodiments, the illumination effect is animpact of illumination by the light source on at least one object in theenvironment. In embodiments, the two-dimensional images include datarepresenting an impact of light from the light source on at least oneobject in the environment. In embodiments, the detecting includesextracting a plurality of data values from the digital data structurebased on the two-dimensional location value and the light sourcedistance value for each of the extracted data values. In embodiments,the light source distance value includes an image identifier. Inembodiments, the image identifier facilitates identifying an image ofthe plurality of images.

In embodiments, methods and systems for identifying a desired lightingsource, includes a library of lighting objects including at least one oflighting fixture objects and light source objects. In embodiments, thelighting objects are characterized by lighting properties. Inembodiments, the lighting properties include at least one output bloomproperty that characterizes a luminance pattern provided by an output ofa lighting object selected from the library; and a pattern matchingsystem that identifies at least one lighting object in the library basedon at least one output bloom property.

In embodiments, the output bloom property includes a shape of the outputbloom. In embodiments, the shape of the output bloom is at a specifieddistance from the lighting object. In embodiments, the shape of theoutput bloom is determined at an intersection of the light bloom with asurface. In embodiments, the surface includes one of a plane, a column,and a slope. In embodiments, the shape of an output bloom includes aportion of a near field illumination pattern generated by a light objectselected from the library.

In embodiments, the shape is a substantially continuous shape. Inembodiments, the shape is a discontinuous pattern. In embodiments, theoutput bloom property includes a portion of at least one of a near fieldillumination pattern and a far field illumination pattern generated by alight object selected from the library. In embodiments, the output bloomproperty includes at least one of a color and an intensity of an outputbloom. In embodiments, the output bloom property includes a reflectionfrom a surface.

In embodiments, the output bloom property includes a transmissionthrough a surface. In embodiments, the surface is a translucent surface.In embodiments, the surface is a shade of a lighting fixture. Inembodiments, the method includes a user interface configured to permit auser to view and select a lighting object based on a display of theoutput bloom.

In embodiments, the method includes a user interface configured topermit a user to at least one of specify and select a desired outputbloom property. In embodiments, the pattern matching systemautomatically matches at least one lighting object in the library to thedesired output bloom property. In embodiments, the pattern matchingsystem is an artificial intelligence classification system. Inembodiments, the artificial intelligence system is trained to matchoutput bloom patterns based on a training set of patterns matched by oneor more human users. In embodiments, the methods and systems ofelectronic display rendering of lighting distribution in athree-dimensional space, include modeling light source emissions as aset of light ray-traces that represent light traveling between a lightsource and an element in the three-dimensional space, and reflectionstherefrom. In embodiments, the modeling of the reflections is based onthe set of ray-traces and at least one reflection characteristic of theelement in the three-dimensional space; capturing the light sourceemissions and the reflections as light volume-data; interpolating atleast one of light source emissions and reflections for a plurality ofpoints in the three-dimensional space; determining interactions amongthe ray-traces and reflections in the three-dimensional space; andrendering in the electronic display the volume-data with theinterpolated plurality of points and the interactions among theray-traces in the three-dimensional space.

In embodiments, modeling includes accounting for at least one of lighttransparency, absorption and reflection of the element in thethree-dimensional space. In embodiments, the electronic display iscontrolled by a virtual reality display controller. In embodiments, theelectronic display is an augmented reality display controlled by anaugmented reality display controller. In embodiments, rendering includesrendering near-field lighting artifacts. In embodiments, the near-fieldlighting artifacts are rendered throughout the three-dimensional space.In embodiments, rendering includes accounting for effects relating tophysical characteristics of the light source. In embodiments, the lightsource includes a plurality of distinct light elements, each distinctlight element being associated with a corresponding set of ray-traces.In embodiments, rendering includes rendering effects from each of theplurality of distinct light elements. In embodiments, rendering includesrendering distance-based light source intensity. In embodiments,rendering distance-based light source intensity includes rendering lightsource intensity fall-off over distance from the light source for eachray-trace in the set of ray-traces.

In embodiments, methods and systems of capturing the light sourceemissions includes disposing a surface at at least one of a plurality ofdistances in proximity to the light source so that a first side of thesurface is illuminated directly by the light source, capturing, with atleast a two-dimensional image sensor, a plurality of luminance valuesfrom at least one side of the illuminated surface; generating a datastructure characterizing the illumination field generated by the lightsource including the pattern of luminance values on at least one side ofthe illuminated surface for the distance among the plurality ofdistances; storing an image captured by the image sensor; and repeatingthe disposing, capturing, and generating a data structure steps for aplurality of incremental distances between the light source and theintermediate surface. In embodiments, the repeating causes storing aplurality of incremental-distance differentiated images of the luminanceof the light source.

In embodiments, methods and systems of electronic display rendering oflighting distribution in a three-dimensional space, includes modelinglight source emissions as a set of light ray-traces that represent lighttraveling between a light source and an element in the three-dimensionalspace, and reflections therefrom. In embodiments, the modeling of thereflections is based on the set of ray-traces and at least onereflection characteristic of the element in the three-dimensional space;capturing a plurality of two-dimensional images of at least one of thelight source emissions and the reflections; storing a portion of theplurality of images in a digital data structure as light volume-data,the structure facilitates distinguishing among the light volume data ineach of the plurality of images by a two-dimensional location in animage of the plurality of images and an effective distance of the imagefrom the light source; interpolating at least one of light sourceemissions and reflections for a plurality of points in thethree-dimensional space; determining interactions among the ray-tracesand reflections in the three-dimensional space; and rendering in theelectronic display the volume-data with the interpolated plurality ofpoints and the interactions among the ray-traces in thethree-dimensional space.

In embodiments, the modeling includes accounting for at least one oflight transparency, absorption and reflection of the element in thethree-dimensional space. In embodiments, the electronic display iscontrolled by a virtual reality display controller. In embodiments, theelectronic display is an augmented reality display controlled by anaugmented reality display controller. In embodiments, the renderingincludes rendering near-field lighting artifacts. In embodiments, thenear-field lighting artifacts are rendered throughout thethree-dimensional space. In embodiments, the rendering includesaccounting for effects relating to physical characteristics of the lightsource. In embodiments, the light source includes a plurality ofdistinct light elements, each distinct light element being associatedwith a corresponding set of ray-traces. In embodiments, the renderingincludes rendering effects from each of the plurality of distinct lightelements. In embodiments, the rendering includes renderingdistance-based light source intensity. In embodiments, the renderingdistance-based light source intensity includes rendering light sourceintensity fall-off over distance from the light source for eachray-trace in the set of ray-traces.

In embodiments, methods and systems for enabling custom tuning alighting object, includes defining a custom tuning profile for thelighting object, the custom tuning profile specifying at least one of acolor tuning profile, a dimming profile, and a lighting distributionprofile for the lighting object; and automatically, under control of aprocessor, translating the defined custom tuning profile into a set ofinstructions for controlling the lighting object to behave according tothe custom tuning profile in response to user input.

In embodiments, the custom tuning profile is a dimming profile thatspecifies a set of points on a color temperature gamut that defines adimming curve along which the lighting object will dim in response to acontrol input from a dimmer. In embodiments, the dimming profile isspecified to match a known dimming profile of a type of lighting object.In embodiments, the custom tuning profile is a color tuning profile thatspecifies a set of points on a color gamut through which a lightingobject will progress in response to a variable voltage control input. Inembodiments, the method includes a user interface configured to permit auser to define the custom tuning profile. In embodiments, the userinterface allows a user to specify a dimming profile by tracing a curveon a gamut. In embodiments, the user interface allows a user to specifya color for a color tuning profile from a color gamut.

In embodiments, the method includes a library of stored profilesselectable by a user for tuning of a lighting object. In embodiments,the library of stored profiles includes at least one of a color qualityprofile, a circadian profile, a concentration profile, a relaxationprofile, and an efficacy profile. In embodiments, the library isorganized to provide custom tuning profiles that satisfy at least one ofa performance requirement and an aesthetic requirement desired by auser. In embodiments, methods and systems of controlling a color of alight source independent of controlling dimming of the light source,include capturing at least one custom color curve for controlling alight source; controlling dimming of the light source through a firstinput that accepts a voltage that varies between 0 and 10 volts;controlling color of the light source through a second input thataccepts a voltage that varies between 0 and 10 volts independent of thefirst input; and mapping the at least one custom color curve to thesecond input range of 0 to 10 volts.

In embodiments, the custom color curve is a dimming profile thatspecifies a set of points on a color temperature gamut that defines adimming curve along which the light source will dim in response to acontrol input from a dimmer. In embodiments, the dimming profile isspecified to match a known dimming profile of a type of lighting object.In embodiments, the custom color curve is a color tuning profile thatspecifies a set of points on a color gamut through which the lightsource will progress in response to a variable voltage control input.

In embodiments, the method includes a library of stored profilesselectable by a user for tuning of a lighting object. In embodiments,the library of stored profiles includes at least one of a color qualityprofile, a circadian profile, a concentration profile, a relaxationprofile, and an efficacy profile. In embodiments, the library isorganized to provide custom tuning profiles that satisfy at least one ofa performance requirement and an aesthetic requirement desired by auser.

In embodiments, methods and systems for a light source control systemfor a light source that has independent color and dimming controlinputs, and include a first output port of the system that isoperatively coupled to the color control input of the light source; asecond output port of the system that is operatively coupled to thebrightness control input of the light source; and a processor of thesystem that accesses a light source tuning profile that characterizes amulti-dimensional lighting curve by mapping color output of the light tobrightness of the light source so that a change in the brightness inputcauses a corresponding change in the color input.

In embodiments, the processor controls the first output and the secondoutput based on information in the tuning profile. In embodiments, thecontrolling the brightness input results in the processor alsocontrolling the color input to adjust the color of the light based onthe tuning profile. In embodiments, the controlling the brightness toreduce the brightness results in a warmer color being output by thelight source. In embodiments, the controlling the brightness to increasethe brightness results in a cooler color being output by the lightsource. In embodiments, the tuning profile maps a plurality of targetcolor and brightness output values to a corresponding plurality oftwo-dimensional voltage values, a first dimension controlling lightcolor and a second dimension controlling brightness. In embodiments, thetuning profile maps values in the first dimension to a color controlinput voltage range. In embodiments, the tuning profile maps values inthe second dimension to a brightness control input voltage range.

In embodiments, the tuning profile maps target output color temperaturesof the light source to values in the first and second dimensions so thatcontrolling the color input and brightness input based on the first andsecond dimensions configures the light source to output a target colortemperature based on the tuning profile color temperature mapping. Inembodiments, the tuning profile facilities maintaining a light color asthe light is dimmed by adjusting the light color control based on achange in the light dimming control. In embodiments, the tuning profileis indexed by at least one of biologic impacts and physiological impactsof light so that at least one of the light color and the lightbrightness is specified for a plurality of biologic impacts andphysiological impacts.

In embodiments, methods and systems of using emotional filters forlighting design, include capturing stylistic and aesthetic features froma visual representation of an environment; populating, with the capturedfeatures, an installation-specific emotional content data structure;applying machine learning to user feedback about at least one ofemotional and aesthetic aspects of installation. In embodiments, theinstallation is characterized by the installation-specific emotionalcontent data structure. In embodiments, the machine learning facilitatesgenerating an understanding of factors that contribute to each emotionaleffect of a plurality of emotional effects; and updating at least aportion of the emotional content data structure based on the feedback.

In embodiments, the visual representation includes at least one of aphotograph and a video. In embodiments, the method includes selecting atleast one light source for the environment based on a similarity of aportion of an emotional content data structure for the light source witha corresponding portion of the installation-specific emotional contentdata structure. In embodiments, capturing features includes analyzing atleast one of images, 3D models, renderings, and scans of theenvironment. In embodiments, populating includes storing at least one ofcultural and geographical data associated with the environment in theinstallation-specific emotional content data structure. In embodiments,the emotional content data structure includes at least one of objects,classes, and properties including lighting properties selected from agroup consisting of distribution of light on lighting space objects,distribution of lights on surfaces, illumination values, color and colortemperature of light sources, spectral content, and fixture type. Inembodiments, lighting space objects include at least one of desks,tables, and workspaces. In embodiments, the spectral content includesquality and intensity of light at certain spectral ranges. Inembodiments, the fixture type includes at least one of modern, retro,industrial, romantic, suspended, embedded, and form factor.

In embodiments, methods and systems of a lighting design system usingemotional filters, include a visual representation of an environment; afeature capture facility adapted to capture stylistic and aestheticfeatures of the environment from the visual representation and populatethe captured features into an installation-specific emotional contentdata structure that is accessible to a processor; machine learningalgorithms executing on a processor that receive user feedback about atleast one of emotional and aesthetic aspects of an installationcharacterized by the installation-specific emotional content datastructure, the machine learning algorithms generating an understandingof factors that contribute to each emotional effect of a plurality ofemotional effects, the processor updating at least a portion of theemotional content data structure based on the feedback.

In embodiments, the visual representation includes at least one of aphotograph and a video. In embodiments, the method includes theprocessor selecting at least one light source for the environment basedon a similarity of a portion of an emotional content data structure forthe light source with a corresponding portion of theinstallation-specific emotional content data structure. In embodiments,the feature capture facility is configured to capture stylistic andaesthetic features by analyzing at least one of images, 3D models,renderings, and scans of the environment. In embodiments, to populatethe captured features includes storing at least one of cultural andgeographical data associated with the environment in theinstallation-specific emotional content data structure.

In embodiments, the emotional content data structure includes at leastone of objects, classes, and properties including lighting propertiesselected from a group consisting of distribution of light on lightingspace objects, distribution of lights on surfaces, illumination values,color and color temperature of light sources, spectral content, andfixture type. In embodiments, lighting space objects include at leastone of desks, tables, and workspaces. In embodiments, spectral contentincludes quality and intensity of light at certain spectral ranges. Inembodiments, the fixture type includes at least one of modern, retro,industrial, romantic, suspended, embedded, and form factor. Inembodiments, the method includes a library of light source emotionalcontent data structures that describe stylistic and aesthetic featuresof a plurality of light sources.

In embodiments, the method includes a light source selection facilitythat compares at least one portion of emotional content data structuresin the library with a corresponding at least one portion of aninstallation-specific emotional content data structure therebygenerating a set of candidate light sources for satisfying at least oneof aesthetic and stylistic aspects of the environment. In embodiments,information descriptive of at least one of the aesthetic and stylisticaspects of the set of candidate light sources is displayed on anelectronic display to enable comparison with each other and with the atleast one of aesthetic and stylistic aspects of the environment.

In embodiments, methods and systems of a near-field characterizationsystem include a light source positioning support adapted to hold alight source disposed to distribute light in a first orientation; anintermediate screen disposed to receive on a first side the distributedlight, the intermediate screen constructed to transfer a portion of thereceived light to a second side that is substantially parallel to thefirst side; a two-dimensional array illumination sensor disposed tocapture an image of the second side of the intermediate screen, theimage including a data value representing illumination at each of aplurality of image sensing positions in the array; a processor adaptedto control the illumination sensor and store the data value and thetwo-dimensional location of the corresponding image sensing position inthe array; and a data storage facility that works with the processor tostore the data value and its corresponding position for a plurality ofimage sensing positions in the array.

In embodiments, to control the illumination sensor includes rotating theillumination sensor. In embodiments, the two-dimensional arrayillumination sensor includes a digital camera. In embodiments, thedigital camera is a camera function of a smartphone. In embodiments, theintermediate screen is translucent.

In embodiments, the method includes a positioning system of theintermediate screen controlled by the processor to adjust a distancebetween the light source and the intermediate screen. In embodiments,the method includes a positioning system of the light source controlledby the processor to adjust a distance between the light source and theintermediate screen. In embodiments, the light source positioningsupport facilitates rotational and translational motion of the lightsource. In embodiments, the processor is further adapted to control atleast one of position, rotation, and translational motion of the lightsource. In embodiments, the method includes a housing that mitigates theimpact of ambient light on the intermediate screen and the area arrayillumination sensor.

In embodiments, methods and systems for a near-field characterizationsystem include a processor controlled light source positioning supportadapted to hold a light source disposed to distribute light in aplurality of orientations, the processor controlling at least a rotationof the light source about a longitudinal axis of the support; anintermediate screen including a first side and a substantially parallelsecond side, the intermediate screen disposed to receive the distributedlight on the first side and constructed to transfer a portion of thereceived light to the second side; an area array illumination sensordisposed to capture light emissions from the second side of theintermediate screen; a controller adapted to control the illuminationsensor and store the data value and the array location of thecorresponding image sensing position in a data storage facility.

In embodiments, the method includes a housing that mitigates the impactof ambient light on the intermediate screen and the area arrayillumination sensor. In embodiments, the housing extends from the secondside of the intermediate screen to the area array. In embodiments, themethod includes a housing that encloses the light source, theintermediate screen, and the area array. In embodiments, the housing isconfigured to conditionally eliminate ambient light from reaching theenclosed system elements. In embodiments, the method includes aspectrometer disposed relative to the intermediate screen to capturespectral content of light proximal to the intermediate screen.

In embodiments, the spectrometer is disposed to capture spectral contentof light between the light source and the intermediate screen. Inembodiments, the spectrometer is disposed to capture spectral content oflight between the intermediate screen and the area array sensor. Inembodiments, a position and orientation of at least one of the lightsource, the intermediate screen, and the area array is adjustable underprocessor control. In embodiments, at least one of the position andorientation of at least one of the light source, intermediate screen,and the area array is adjusted between successive area array lightdistribution captures. In embodiments, an increment of the adjustmentbetween successive light distribution captures is non-linear. Inembodiments, an increment after a light distribution capture is based onan at least one of an entropy and an amount of information captured.

In embodiments, methods and systems for characterizing a near fieldillumination effect of a light source, the method includes iterativelycapturing, with a multi-dimensional image sensor set, an illuminationvalue for each of a plurality of image sensing elements in the imagesensor set, for a plurality of distance-specific positions of the lightsource; storing, in a processor accessible electronic memory, aplurality of images captured by the image sensor set; producing amulti-dimensional representation of the near-field light distribution oflight source by processing, with a multi-dimensional near-fieldillumination reconstruction algorithm, the plurality of stored imagesand their corresponding distance-specific position values; and storingthe multi-dimensional representation in the processor accessibleelectronic memory.

In embodiments, the image sensor set is a two-dimensional array. Inembodiments, the multi-dimensional representation includes fourdimensions consisting of a first dimension of the two-dimensional array,a second dimension of the two-dimensional array, a theta component ofthe corresponding distance-specific position value and a phi componentof the corresponding distance-specific position value.

In embodiments, the multi-dimensional representation includes fivedimensions consisting of a first dimension of the two-dimensional array,a second dimension of the two-dimensional array, a value representingthe distance-specific position of the light source, a theta component ofthe corresponding distance-specific position value and a phi componentof the corresponding distance-specific position value. In embodiments,the reconstruction algorithm determines a relative contribution of eachpoint source on a light source's surface to each pixel in thetwo-dimensional array image sensor. In embodiments, the producing amulti-dimensional representation includes applying at least one of theKaczmarz method, numerical methods, machine learning methods, neuralnetworks, and linear algebra. In embodiments, the multi-dimensionalarray image sensor set includes a smartphone camera. In embodiments, themulti-dimensional representation constitutes a high-fidelity model ofthe light source. In embodiments, the method includes controlling withthe processor, a distance between the light source and themulti-dimensional array image sensor set. In embodiments, iterativelycapturing includes capturing a light pattern visible on a secondary sideof a translucent intermediate screen disposed between the light sourceand the array image sensor.

In embodiments, methods and systems for incrementally reconstructing anear-field illumination effect of a light source, the method includescapturing a first occurrence of multi-dimensional luminance of a lightsource with an indirect luminance collection device disposed at a firstposition relative to the light source; capturing a second occurrence ofmulti-dimensional luminance of the light source with the indirectluminance collection device disposed at a second position relative tothe light source; producing a representation of the near-fieldillumination of the light source by applying a multi-dimensionalnear-field illumination reconstruction algorithm to the capturedoccurrences of multi-dimensional luminance of the light; storing therepresentation in a computer accessible non-volatile memory; andrepeating the capturing, producing, and storing steps for a plurality ofpositions relative to the light source, thereby producing a model ofnear-field illumination of the light source.

In embodiments, the model of near-field illumination includes aplurality of data values for theta and phi luminance values for aplurality of three-dimensional locations in the near-field of the lightsource. In embodiments, a position relative to the light source includesa distance from the light source, a longitude relative to the lightsource and a latitude relative to the light source. In embodiments, theplurality of positions includes a plurality of distances for a givenlongitude and latitude. In embodiments, the plurality of positionsincludes a plurality of longitudes for a given distance. In embodiments,the plurality of positions includes a plurality of latitudes for a givendistance. In embodiments, the reconstruction algorithm determines acontribution of a point source on a surface of the light source for eachcaptured occurrence.

In embodiments, producing a representation of the near-fieldillumination includes applying at least one of the Kaczmarz method,numerical methods, machine learning methods, neural networks, and linearalgebra to the captured occurrences of multi-dimensional luminance ofthe light. In embodiments, producing a representation of the near-fieldillumination includes applying at least two of the Kaczmarz method,numerical methods, machine learning methods, neural networks, and linearalgebra to the captured occurrences of multi-dimensional luminance ofthe light. In embodiments, the indirect luminance collection deviceincludes a smartphone camera adapted to capture indirect luminance fromthe light source. In embodiments, the smartphone camera adapted with ascreen attached to the smartphone over the smartphone camera so thatlight from the light source impacts the smartphone camera indirectly.

In embodiments, a portion of light from the light source passes throughthe screen. In embodiments, the multi-dimensional representationconstitutes a high-fidelity model of the light source. In embodiments,the method includes controlling with the computer, a distance betweenthe light source and the indirect luminance collection device. Inembodiments, capturing the occurrences of multi-dimensional luminance ofa light source includes capturing a light pattern visible on a secondaryside of a translucent intermediate screen disposed between the lightsource and the indirect luminance collection device.

In embodiments, the near-field illumination reconstruction algorithmproduces a five-dimensional representation of the near-field. Inembodiments, each value in the near-field is characterized by (i) adistance from a reference position on the indirect luminance collectiondevice to the light source, (ii) a longitudinal offset from thereference point for the occurrence, (iii) a latitudinal offset from thereference point, (iv) a theta value of the illumination, and (v) a phivalue for the illumination.

In embodiments, methods and systems include receiving a data structurerepresentative of a desired lighting effect created by the incidence ofillumination from a light source on at least one surface; determiningcharacteristics and values thereof of a light source for producing thedesired lighting effect; matching the light source characteristics to alibrary of light sources. In embodiments, at least a portion of thelight sources in the library includes at least a portion of the lightsource characteristics; determining, from a result of the matching, acandidate set of light sources in the library; selecting a portion ofthe candidate set of light sources based on similarity of the values ofthe determined characteristics with values of correspondingcharacteristics in the candidate set of light sources; and presentingthe selected light sources in an electronic user interface.

In embodiments, the data in the data structure includes a plurality ofluminance values for a lighting effect region. In embodiments, the datain the data structure includes at least one of desired effect of thelighting effect, an aesthetic filter effect of the lighting effect andan emotional filter effect of the lighting effect. In embodiments, thecharacteristics include light color and light intensity. In embodiments,the electronic user interface facilitates visual comparison of thedesired lighting effect and a lighting effect of at least one of theselected light sources. In embodiments, the electronic user interfacefacilitates presenting the desired lighting effect and a lighting effectof at least one of the selected light sources in an environment. Inembodiments, the environment is a live view of an environment and theuser interface utilizes augmented reality to present at least one of thedesired lighting effect and a lighting effect of at least one of theselected light sources. In embodiments, the luminance values in theplurality of luminance values are dispersed throughout the lightingeffect region. In embodiments, the lighting effect region issubstantially planar. In embodiments, the user interface further enablesa user to search through the library based on the desired lightingeffect.

In embodiments, the determining characteristics and values thereof of alight source for producing the desired lighting effect is based on aresult of machine learning applied to an algorithm that associates lightsource characteristics with lighting effects. In embodiments, selectinga portion of the candidate set of light sources based on similarity ofthe values of the determined characteristics employs weighting of thecharacteristics. In embodiments, the weighting is determined based on adegree of compliance by the light sources with the desired lightlighting effect. In embodiments, a user specifies the degree ofcompliance through the user interface.

In embodiments, methods and systems include receiving a data structurerepresentative of a desired lighting effect; determining characteristicsand values thereof of the desired lighting effect; matching thecharacteristics to corresponding lighting effect characteristics in alibrary of lighting effects. In embodiments, each of the lightingeffects in the library corresponds to a light source; determining froman output of the matching a candidate set of light sources in thelibrary; selecting a portion of the candidate set of light sources basedon similarity of the values of the determined characteristics withvalues of corresponding characteristics of lighting effects for lightsources in the candidate set of light sources; and presenting theselected light sources in an electronic user interface.

In embodiments, the data in the data structure includes a plurality ofluminance values for a lighting effect region. In embodiments, the datain the data structure includes at least one of desired effect of thelighting effect, an aesthetic filter effect of the lighting effect andan emotional filter effect of the lighting effect. In embodiments, thecharacteristics include light color and light intensity. In embodiments,the electronic user interface facilitates visual comparison of thedesired lighting effect and a lighting effect of at least one of theselected light sources. In embodiments, the electronic user interfacefacilitates presenting the desired lighting effect and a lighting effectof at least one of the selected light sources in an environment.

In embodiments, the environment is a live view of an environment and theuser interface utilizes augmented reality to present at least one of thedesired lighting effect and a lighting effect of at least one of theselected light sources. In embodiments, luminance values in theplurality of luminance values are dispersed throughout the lightingeffect region. In embodiments, the lighting effect region issubstantially planar. In embodiments, the user interface further enablesa user to search through the library based on the desired lightingeffect. In embodiments, the determining characteristics and valuesthereof for producing the desired lighting effect is based on a resultof machine learning applied to an algorithm that associatescharacteristics with lighting effects.

In embodiments, selecting a portion of the candidate set of lightsources based on similarity of the values of the determinedcharacteristics employs weighting of the characteristics. Inembodiments, weighting is determined based on a degree of compliance bythe light sources with the desired lighting effect. In embodiments, auser specifies the degree of compliance through the user interface. Inembodiments, methods and systems include collecting data from aplurality of lighting installations; and classifying the lightinginstallations based on at least one lighting effect created by theinstallations; and storing at least one property for at least one of alighting object based on the classification of the effect.

In embodiments, the method includes a library of lighting objects forwhich effects are classified. In embodiments, the method includesenabling a user to search for a lighting object based on a desiredeffect sought by the user. In embodiments, classifying is based on ameasured effect on at least one of an individual and a group. Inembodiments, the measured effect is a productivity effect. Inembodiments, the measured effect is a health effect.

In embodiments, classifying includes classifying images of the lightinginstallations to establish at least one of an aesthetic filter and anemotional filter that characterizes a subset of the lightinginstallations. In embodiments, classifying occurs by an expert system.In embodiments, classifying occurs by an artificial intelligence system.In embodiments, the artificial intelligence system is trained based on atraining set created by having human individuals classifying thelighting installations.

In embodiments, the filter includes a data structure indicating weightsfor lighting object properties that contribute to the filter. Inembodiments, the method includes characterizing at least one lightingobject property based on its contribution to the filter. In embodiments,the method includes characterizing at least one lighting object based onits contribution to the filter. In embodiments, methods and systems oflighting, include receiving as a user selection of a filter, an intentof the user; converting the user intent to a set of lighting controlparameters; using a lighting control platform to adjust settings on aplurality of light sources in a target environment to reflect the set oflighting control parameters; applying user feedback associated withdistributions of lighting in the target environment to a machinelearning processor to facilitate developing an understanding of arelationship between user reactions of the lighting environment and theuser's intent; and updating, based on the understanding, a data set thatfacilitates the converting of user intent to lighting controls.

In embodiments, a user intent is to promote user feedback indicative ofone of alertness, happiness, and romance. In embodiments, methods andsystems of near field metrics for evaluating light sources, includestaking a near field illumination characterization of a light source;processing the characterization with at least one of a pattern detectingalgorithm and an artifact detecting algorithm; counting occurrences ofdetected patterns and artifacts; determining at least one of size andscale of detected artifacts; and aggregating at least one of size ofartifacts, scale of artifacts, and occurrences of artifacts, therebyproducing at least one near field metric of a plurality of distinct nearfield metrics for the light source.

In embodiments, the near field metrics are selected from a groupconsisting of a mixing distance metric, a scale of artifacts metric, anda contrast in near field metric. In embodiments, the scale of artifactsmetric includes an indication of at least one of a size, a scale, and afrequency of occurrence of artifacts in a light pattern produced by thelight source. In embodiments, mixing distance metric includes anindication of a distance from a light source at which a magnitude ofartifacts drops below a threshold of artifact visibility. Inembodiments, contrast in near field metric includes an indication of anintensity of at least one of patterns and artifacts detectable proximalto the light source. In embodiments, the indication of intensityincludes a minimum to maximum ratio of at least one of detectablepatterns and artifacts.

In embodiments, methods and systems for providing near field metrics forcharacterizing light sources, include accessing a data structure thatcaptures a near field illumination characterization of a light source;calculating metrics for the near field including at least one of lightquality rating, light output, color range, color temperature, lightingmixing characteristics and spectral characteristics by processing atleast two of three-dimensional position, theta, and phi values for aplurality of data values in the data structure with near-field metricsalgorithms; and storing the calculated metrics in a library of lightsources so that accessing the light source in the library facilitatesaccessing the associated near field metrics.

In embodiments, near field metrics are selected from a group consistingof a mixing distance metric, a scale of artifacts metric, and a contrastin near field metric. In embodiments, the scale of artifacts metricincludes an indication of at least one of a size, a scale, and afrequency of occurrence of artifacts in a light pattern produced by thelight source. In embodiments, mixing distance metric includes anindication of a distance from a light source at which magnitude ofartifacts drops below a threshold of artifact visibility. Inembodiments, contrast in near field metric includes an indication of anintensity of at least one of patterns and artifacts detectable proximalto the light source. In embodiments, the indication of intensityincludes a minimum to maximum ratio of at least one of detectablepatterns and artifacts. In embodiments, calculating metrics is based onmachine learning algorithms applied to algorithms that associatecandidate metrics with a plurality of near field data sets.

In embodiments, methods and systems for augmented reality lightingmethods, include a first device representing a light source, a positionand orientation of the first device in an environment being detectableby a second device in the environment; the second device capturing animage of at least a portion of the environment based on the detectedposition and orientation of the first device and communicating thedetected position and orientation of the first device and the capturedimage over a network to a lighting modeling server; the lightingmodeling server accessing a lighting model of the light source andmodeling an interaction of the light source with elements of theenvironment detected in the captured image based on the position andorientation of the first device; and the second device receiving themodeled interaction from the lighting modeling server and rendering themodeled interaction in an augmented reality representation of theenvironment.

In embodiments, the second device detects the orientation and positionof the first device by capturing at least one image of the first devicein the environment, analyzing the at least one image for indications ofthe position of the device and its orientation in the environment, andtracking changes to the position and orientation of the device. Inembodiments, the second device detects the orientation and position ofthe first device by analyzing received position and orientationinformation from the first device. In embodiments, the first deviceincludes a user interface through which a user is enabled to select alight source from a library of light sources and through which an imageof the selected light source is presented. In embodiments, the firstdevice is configured to communicate an identifier of the selected lightsource to the lighting model server. In embodiments, the lighting modelserver accesses the lighting model for the selected light source fromthe library.

In embodiments, methods and systems include a first computing devicedisposed in an environment and rendering in its user interface aselected light fixture, the first device communicating its location andorientation in the environment over a wireless network; and a seconddevice rendering in its user interface an illumination effect of theselected light fixture on a portion of the environment in response to amodel of luminance of the selected light fixture, at least one ofsurfaces and objects in the portion of the environment, and the locationand orientation of the first device.

In embodiments, the second device captures at least one image of theportion of the environment based on the location and orientation of thefirst device. In embodiments, the second device is disposed in theenvironment. In embodiments, changes to at least one of the position andorientation of the first device cause corresponding changes to therendering of the illumination effect in the second device. Inembodiments, the model of luminance incorporates at least one ofnear-field and far-field luminance characterization of the selectedlight fixture.

In embodiments, the second device includes an augmented reality devicethat renders an illumination effect of the selected light fixture basedat least in part on a position and orientation of the second device inthe environment. In embodiments, the user interface of the first deviceis configured to facilitate selecting a light fixture from a lightfixture library. In embodiments, the method includes a lighting spacemodeling server that generates a data set that describes theillumination effect of the selected light fixture on the portion of theenvironment that the second device uses for rendering. In embodiments,the first device is configured to communicate an identifier of theselected light fixture to the lighting space model server. Inembodiments, the lighting space model server accesses the model ofluminance of the selected light fixture from the library.

In embodiments, methods and systems of augmented reality-based lightingdesign, include detecting light sources in an augmented reality image;detecting at least one of surfaces and objects in the augmented realityimage; facilitating disposition of at least one virtual light source inthe augmented reality image, resulting in an updated augmented realityimage; processing a near field and far field luminance characterizationof the at least one virtual light source and the updated augmentedreality image with a lighting space model; and depicting illumination ofportions of the augmented reality image in response to the lightingspace model.

In embodiments, portions of the augmented reality image include at leastone of the detected surfaces and objects. In embodiments, the methodincludes detecting lighting effects of the detected light sources. Inembodiments, illuminating portions of the augmented reality image is inresponse to the detected lighting effects.

In embodiments, methods and systems of illumination in an environment,include controlling a first light source in the environment, disposed toilluminate a first region of the environment, to mimic sky color basedon at least one of a user input and a time of day; and controlling asecond light source to mimic a window on a vertical wall of theenvironment. In embodiments, the second light source generates a coloron the vertical wall that is consistent with the mimicked sky color.

In embodiments, the illuminated first region of the environment includesan area of a ceiling of the environment. In embodiments, the illuminatedfirst region of the environment mimics a skylight. In embodiments, theilluminated first region of the environment includes a plurality ofdistinct areas of a ceiling of the environment. In embodiments, theilluminated first region of the environment mimics a plurality ofdistinct skylights. In embodiments, the illuminated first region changescolors based on an estimated position of the sun from sunrise to sunset.In embodiments, the illuminated first region mimics a lunar illuminationeffect based on a position of the moon. In embodiments, the methodincludes controlling a third light source to produce a melanopic fluxratio of at least 10:1 in a portion of the environment.

In embodiments, the first light source is disposed in the environment toproduce cove lighting. In embodiments, the at least one of the firstlight source and the second light source is disposed in the environmentto produce graze lighting. In embodiments, controlling the first lightsource and the second light source results in a melanopic flux ratio ofat least 10:1 in a portion of the environment.

In embodiments, methods and systems of illumination in an environment,include controlling a first light source in the environment, the firstlight disposed to illuminate a first region of the environment, thecontrolled light mimicking sky color based on at least one of a userinput and a time of day; and controlling a second light source toilluminate a second region of the environment, the second light sourcebeing selected from a library of light sources. In embodiments, thesecond region of the environment is a workspace.

In embodiments, the illuminated first region of the environment includesan area of a ceiling of the environment. In embodiments, the illuminatedfirst region of the environment mimics a skylight. In embodiments, theilluminated first region of the environment includes a plurality ofdistinct areas of a ceiling of the environment. In embodiments, theilluminated first region of the environment mimics a plurality ofdistinct skylights. In embodiments, the illuminated first region changescolors based on an estimated position of the sun from sunrise to sunsetand based on an estimated position of the moon from sunset to sunrise.In embodiments, the illuminated first region mimics a lunar illuminationeffect based on a position of the moon. In embodiments, the methodincludes controlling a third light source to produce a melanopic fluxratio of at least 10:1 in a portion of the environment.

In embodiments, the first light source is disposed in the environment toproduce cove lighting. In embodiments, the at least one of the firstlight source and the second light source is disposed in the environmentto produce graze lighting. In embodiments, controlling the first lightsource and the second light source results in a melanopic flux ratio ofat least 10:1 in a portion of the environment.

In embodiments, methods and systems of illumination in an environment,include controlling a downlight light source in the environment to mimicsky color for at least one of sunrise, mid-day sun, and sunset; andcontrolling an uplight light source in the environment in response tothe downlight control so that the illumination in the environmentproduces a melanopic flux ratio of at least 10:1 in the environment.

In embodiments, control of the uplight light source includes adjustingat least two channels of a multiple channel light. In embodiments,controlling the uplight source further produces a circadian action. Inembodiments, the method includes controlling the downlight light sourceto shift a bias of light in the environment toward at least a firstside, a central portion, and a second side of the environment. Inembodiments, the environment is a room. In embodiments, coordinatingcontrol of the uplight light source in the environment in response tothe downlight control includes mimicking sky color for at least one ofsunrise, mid-day sun, and sunset.

In embodiments, methods and systems include receiving biomarkerinformation from a plurality of wearable sensors from at least one userin a lighting control environment over a time frame; recording controlsettings for at least one light in the lighting control environment overthe time frame; and using machine learning to determine correlationsbetween biological states of the user and lighting effects in theenvironment based on the biomarker information and the record oflighting control settings.

In embodiments, methods and systems include receiving estimated futuretime zone and an activity schedule of a user for a plurality ofsequential days; identifying an estimated future time zone of the userthat is different than a current time zone; and controlling at least onelight proximal to the user according to the estimated different futuretime zone prior to the user being located in the different estimatedtime zone.

In embodiments, methods and systems include receiving architectureinformation for a space into a lighting design system; processing thearchitecture information with a three-dimensional physical spacemodeling algorithm that produces a model of the space including at leastone of objects and surfaces in the space; and applying a model ofluminance for a light fixture to the model of the space, therebyproducing an emulation of luminance in the space including an impact ofluminance from the light fixture on at least one of the objects andsurfaces in the space.

In embodiments, the method includes lighting control system that usesmachine learning to adapt light control settings for at least one lightin an environment based on at least one of a schedule of time zones of auser in the environment, a schedule of activities of the user in theenvironment, biomarker information received from wearable sensors wornby at least one user in the environment, feedback on lighting effectscaused by the light control settings from users in the environment, anduser generated light control settings for at least one light in theenvironment.

In embodiments, methods and systems of configuring a three-dimensionalspace for lighting simulation include capturing information descriptiveof physical aspects of an environment as a three-dimensional point-cloudrepresentation of the physical aspects; applying machine learning to thedescriptive information to detect architectural features of theenvironment; determining light modeling aspects of the detectedarchitectural features; configuring a digital library of thearchitectural features including at least one of the light modelingaspects of each architectural feature in the library; and configuring alighting space model of the environment that references the library ofarchitectural features and incorporates corresponding light modelingaspects of architectural features referenced in the library.

In embodiments, capturing information descriptive of the physicalaspects of an environment includes use of one or more of a digitalcamera, three-dimensional sensor, camera-equipped personal computingdevice capturing images of the environment. In embodiments, capturinginformation descriptive of the physical aspects of the environmentincludes generating measurements of the detected architectural featuresand distances between them. In embodiments, applying machine learning tothe descriptive information includes processing point clouds of theenvironment. In embodiments, the method includes generating at least oneof a floor plan and a reflected ceiling plan of the environment.

In embodiments, the method includes presenting the lighting space modelin an artificial reality interface. In embodiments, the method includesdetecting at least one light source in the environment. In embodiments,configuring a lighting space model incorporates light modeling aspectsof the light source. In embodiments, configuring a lighting space modelincludes incorporating a light source model for at least one lightsource in the environment. In embodiments, the light modeling aspects ofthe detected architectural features include reflectance by the featureof light directed a first angle. In embodiments, the light modelingaspects of the detected architectural features include surface type forat least one surface of the feature.

In embodiments, methods and systems of configuring a three-dimensionalspace model for lighting simulation include capturing visual informationrepresentative of physical aspects of an environment as athree-dimensional visual representation of the environment; detecting atleast one of surfaces and edges between surfaces in the visualrepresentation; determining physical relationships among the detectedsurfaces and edges. In embodiments, the physical relationships includerelative orientation of a plurality of the detected surfaces; analyzingan impact of illumination on at least one of the surfaces and the edgesto generate a reflective model of the analyzed surfaces and edges; andconfiguring a lighting space model of the environment that incorporatesthe detected surfaces and edges, their orientations, and theirreflective model.

In embodiments, capturing visual information representative of thephysical aspects of the environment includes use of one or more of adigital camera, three-dimensional sensor, camera-equipped personalcomputing device to capture at least one image of a portion of theenvironment. In embodiments, determining physical relationships includesgenerating measurements of surfaces and distances between the surfaces.In embodiments, the method includes applying machine learning to anoutput of the analyzing an impact of illumination on at least one of thesurfaces and the edges to improve generating the reflective model.

In embodiments, the method includes generating at least one of a floorplan and a reflected ceiling plan of the environment. In embodiments,the method includes presenting the lighting space model in an artificialreality interface. In embodiments, the method includes detecting atleast one light source in the environment. In embodiments, configuringthe three dimensional space model includes incorporating light modelingaspects of the light source.

In embodiments, configuring the three dimensional space model includesincorporating a light source model for at least one light source in theenvironment. In embodiments, the reflective model of the analyzedsurfaces and edges includes reflectance by the surface of light directeda first angle. In embodiments, the reflective model of the analyzedsurfaces and edges includes a surface type for at least one of thesurfaces.

In embodiments, methods and systems include receiving from a serverblockchain-secured digital image content representative of a lowresolution of an environment; rendering, via processing the receivedcontent, a low resolution image of an impact of a light disposed at afirst location and oriented in a first orientation in the environment onelements in the environment; and in response to receiving an indication,from a user interface on which the low resolution image is rendered, ofa subset of the environment to render in high resolution, rendering inhigh resolution and transmitting a blockchain-secured digital image ofthe indicated subset of the environment.

In embodiments, rendering the low resolution version is performed by amobile device and wherein the rendering in high resolution is performedby a networked server. In embodiments, the method includes displayingthe transmitted high resolution digital image on a user interface of amobile device receiving the blockchain-secured transmitted highresolution image. In embodiments, the digital image contentrepresentative of the subset includes a full geometric model of theindicated subset.

In embodiments, the digital image content representative of the subsetincludes a high resolution image of the impact of the light on theelement in the environment. In embodiments, the low resolution versionis rendered in a virtual reality display of the environment. Inembodiments, the digital image content includes at least one of a nearfield illumination model of illumination produced by a light source anda far field illumination model of illumination produced by the lightsource.

In embodiments, the blockchain secures a low resolution image of aportion of the environment rendered on a mobile device and a highresolution image of the portion of the environment rendered on acomputing server device. In embodiments, methods and systems includereceiving a first blockchain-secured digital image contentrepresentative of an environment; rendering, via processing the receivedcontent, a first resolution version of an impact on elements in theenvironment of a light disposed at a first location and oriented in afirst orientation in the environment; receiving subsequentblockchain-secured digital image content of the environment; rendering asubsequent resolution version of the impact of light on the elements bycombining the received subsequent digital image content with the mostrecently rendered resolution version; and repeating the receivingsubsequent and rendering a subsequent resolution version until at leasta portion of the next resolution version includes a resolutionequivalent to a high resolution version of the environment.

In embodiments, rendering the first resolution version is performed by amobile device and wherein the rendering of at least one of thesubsequent resolution versions is performed by a networked server andstreamed to the mobile device. In embodiments, the digital image contentof the environment includes a full geometric model of the indicatedsubset. In embodiments, the next resolution version of the impact oflight on the elements includes a high resolution image. In embodiments,the first content is rendered in a virtual reality display of theenvironment.

In embodiments, methods and systems include receiving on a firstcomputing device a first multi-dimensional image of illuminationproduced by a light source and captured with an indirect near fieldillumination multi-dimensional image capture device; rendering on a userinterface of the first computing device a first resolutionrepresentation of the near field illumination; receiving additionalposition differentiated multi-dimensional images of illumination fromthe light source captured by the indirect near field capture device; andin response thereto, rendering increasingly higher resolutionrepresentations of the near field illumination.

In embodiments, a count of additional position differentiatedmulti-dimensional images received is limited based on a performanceaspect of the first computing device. In embodiments, a count ofadditional position differentiated multi-dimensional images received andrendered by a mobile device is less than a count of additional positiondifferentiated multi-dimensional images received and rendered by aserver. In embodiments, rendering at least one of the increasinglyhigher solution representations of the nearfield illumination isperformed by a computing device other than the first computing device.

In embodiments, receiving a first multi-dimensional image includesreceiving a blockchain-secured message including the firstmulti-dimensional image. In embodiments, receiving additionalmulti-dimensional images includes receiving a blockchain-secured messageincluding at least one of the additional multi-dimensional images.

In embodiments, methods and systems of producing a color tuning curveinclude controlling a first color control channel including setting an“x” value that corresponds to a first axis of a CIE diagram, based on asecondary control input; controlling a second color control channelincluding setting a “y” value that corresponds to a second axis of a CIEdiagram, based on a secondary control input; controlling a third colorcontrol channel including setting a dim value that corresponds to alight output value; and controlling a fourth color control channelincluding setting at least one of the “x” value and the “y” value basedon a primary control input.

In embodiments, the method includes producing the color tuning curve inan augmented reality lighting simulation environment. In embodiments, alighting effect resulting from producing the color tuning curve isrendered throughout a three-dimensional space. In embodiments, the colortuning curve is applied to a lighting source in the three-dimensionalspace. In embodiments, the rendering includes accounting for effectsrelating to physical characteristics of light source. In embodiments,the rendering includes rendering distance-based light source intensity.

In embodiments, the rendering distance-based light source intensityincludes rendering light source intensity fall-off over distance fromthe light source for each ray-trace in the set of ray-traces. Inembodiments, the method includes providing a user interface thatfacilitates a user selecting a lighting fixture to control to producethe color tuning curve. In embodiments, the method includes providing auser interface that facilitate a user selecting at least one of aprogrammable dimming curve, programmable color tuning curve, a tuningcurve start point, a tuning curve end point, a tuning curve dimmingpath, and a color tuning path.

In embodiments, in at least one of the turning curve dimming path andcolor tuning path is responsive to a level of dimming. In embodiments,the controlling steps are applied to a lighting system including threewhite light sources each with different CCTs to produce the color tuningcurve. In embodiments, controlling steps are applied to a lightingsystem including a plurality of different color light emitting diode(LED) light sources to produce the color tuning curve. In embodiments,producing the color tuning curve is responsive to a user selection of atuning curve start point, tuning curve end point and at least one tuningcurve waypoint between the start and end points.

In embodiments, methods and systems for producing a single color oflight across a plurality of color modes, include a four channel lightemitting diode (LED) illumination source. In embodiments, each of thefour channels are independently controllable for at least an amount oflight output by the corresponding light emitting diode in theillumination source; a set of mathematical models that define featuresof each of a plurality of the color modes that, when processed with amap of LED illumination source channel control values for a plurality oftarget illumination colors by a processor produces a set of intensityinformation for each of the plurality of target illumination colors; anda computing architecture of the illumination source that receives anindication of a target color and a color mode and controls the fourchannels of the illumination source to produce the target color based onthe set of intensity information and the indicated color mode.

In embodiments, a target color produced in power efficiency color modeis substantially the same color produced in a full power color mode. Inembodiments, a common target color is produced by the system for each ofa plurality of color modes consisting of a color quality mode, anefficacy mode, a circadian mode, a color bias mode, and a rest mode. Inembodiments, a color quality mode is achieved by maximizing at least oneof the color rendering index (CRI) and fidelity and gamut metrics.

In embodiments, an efficacy mode is achieved by maximizing output lumensper watt of consumed power. In embodiments, a circadian mode is achievedby maximizing equivalent melanopic lux (EML) content. In embodiments, acolor bias mode is achieved by oversaturating a single color as aspectral component of a two-dimensionally indexed position on a colorrendering index diagram. In embodiments, the rest mode is achieved byminimizing at least one of blue illumination and EML content.

In embodiments, the set of mathematical models facilitate producing acolor tuning curve responsively to a user selection of a tuning curvestart point, tuning curve end point and at least one tuning curvewaypoint between the start and end points.

In embodiments, methods and systems of model-based rendering near-fieldeffects of a light source, include modeling light source emissions as aset of direction-specific light ray-traces; capturing data at aplurality of positions along a portion of the set of direction-specificlight ray-traces; determining interactions among the ray-traces; andrendering in an electronic display near-field effects of the lightsource, the effects derived from a lighting space model thatincorporates the light volume-data, the interpolated plurality of pointsand the interactions among the ray-traces.

In embodiments, the lighting space model accounts for at least one oflight transparency, absorption and reflection of elements in thethree-dimensional space. In embodiments, the electronic display iscontrolled by a virtual reality display controller. In embodiments, theelectronic display is an augmented reality display controlled by anaugmented reality display controller. In embodiments, the renderingincludes rendering near-field lighting artifacts. In embodiments, thenear-field lighting artifacts are rendered throughout thethree-dimensional space. In embodiments, the rendering includesaccounting for effects relating to physical characteristics of a sourceof the light emissions. In embodiments, a light source of the lightemissions includes a plurality of distinct light elements, each distinctlight element being associated with a corresponding set of ray-traces.

In embodiments, the rendering includes rendering effects from each ofthe plurality of distinct light elements. In embodiments, the renderingincludes rendering distance-based light source intensity. Inembodiments, the rendering distance-based light source intensityincludes rendering light source intensity fall-off over distance fromthe light source for each ray-trace in the set of ray-traces.

In embodiments, storing the captured data in a computer accessiblememory as three-dimensional light volume-data and interpolating lightsource emissions for a plurality of points in a three-dimensional spacecharacterized by the three-dimensional light volume data that are notpresent in the light volume-data.

In embodiments, the three-dimensional light volume data includes a shapeof a lighting effect from a light source of the light emissions. Inembodiments, a lighting effect property includes a shape of the lightingeffect at a specified distance from the light source. In embodiments,the shape is a substantially continuous shape. In embodiments, the shapeis a discontinuous pattern. In embodiments, the near-field data effectsof the light source include at least one of a color and an intensity ofa lighting effect. In embodiments, the near field data effects of thelight source include a reflection from a surface.

In embodiments, methods and systems of the model-based rendering of alight field, include capturing a set of data representing athree-dimensional space proximal to a light source, the data setincluding data representing illuminance values of light at each of aplurality of locations in the three-dimensional space; extracting amulti-dimensional portion of the set of data; and generating a geometricmodel of the portion that facilitates modelling an impact of theilluminance of the light source on objects disposed in the spaceproximal to the light source.

In embodiments, the methods include interpolating a plurality ofadditional illuminance values within the multi-dimensional portion ofthe set of data. In embodiments, the three-dimensional data set includesa reflection from a surface. In embodiments, methods and systems forplanning lighting in an augmented reality display, include representingphysical features of an environment as a point cloud; using machinelearning to generate a lighting space model of the environment from thepoint cloud; using the lighting space model to produce at least one of afloor plan and a reflected ceiling plan of the environment; coupling thelighting space model to an augmented reality view of the environment. Inembodiments, light sources are added to the lighting space model by auser placing light sources in the augmented reality view of theenvironment; and rendering the environment through the augmented realitydisplay including lighting effects of the placed light sources based ona near field illumination characterization of the placed light sources.

In embodiments, the rendering includes accounting for at least one oflight transparency, absorption and reflection of at least one element inthe environment. In embodiments, the rendering includes renderingnear-field lighting artifacts. In embodiments, the near-field lightingartifacts are rendered throughout the three-dimensional space. Inembodiments, the rendering includes accounting for effects relating tophysical characteristics of at least one light source.

In embodiments, the at least one light source includes a plurality ofdistinct light elements, each distinct light element being associatedwith a corresponding set of ray-traces. In embodiments, the renderingincludes rendering effects from each of the plurality of distinct lightelements. In embodiments, the lighting effects of the placed lightsources are based on an area-source model of the placed light sources.In embodiments, placing light sources includes selecting a model of alight source from a catalog of light sources presented in the augmentedreality environment and indicating at least one of a position andorientation of the light source in the environment. In embodiments, therendering is performed by a volumetric renderer.

In embodiments, methods and systems for planning lighting in anaugmented reality display, include representing physical features of anenvironment as surfaces and edges; using machine learning to generate alighting space model of the environment from the surfaces and edges. Inembodiments, the lighting space model includes attributes for at leastone of absorption and reflectance of each of the surfaces; using thelighting space model to produce a floor plan of the environment;coupling the lighting space model to an augmented reality view of theenvironment. In embodiments, light sources are added to the lightingspace model and to the produced floor plan by a user placing lightsources in the augmented reality view of the environment; and renderingthe environment through the augmented reality display including lightingeffects of the placed light sources based on a near field illuminationcharacterization of the placed light sources.

In embodiments, the rendering includes accounting for at least one oflight transparency, absorption and reflection of at least one surface inthe environment. In embodiments, the rendering includes renderingnear-field lighting artifacts. In embodiments, the near-field lightingartifacts are rendered throughout the three-dimensional space. Inembodiments, the rendering includes accounting for effects relating tophysical characteristics of at least one light source. In embodiments,the at least one light source includes a plurality of distinct lightelements, each distinct light element being associated with acorresponding set of ray-traces. In embodiments, the rendering includesrendering effects from each of the plurality of distinct light elements.

In embodiments, the lighting effects of the placed light sources arebased on an area-source model of the placed light sources. Inembodiments, placing light sources includes selecting a model of a lightsource from a catalog of light sources presented in the augmentedreality environment and indicating at least one of a position andorientation of the light source in the environment. In embodiments, therendering is performed by a volumetric renderer. In embodiments,producing a floor plan includes use of a measuring facility in the ARinterface to measure a space portrayed therein.

In embodiments, methods and systems for planning lighting in anaugmented reality display, include representing physical features of anenvironment as a point cloud; using machine learning to generate alighting space model of the environment from the point cloud; using thelighting space model to produce at least one of a floor plan and areflected ceiling plan of the environment; coupling the lighting spacemodel to an augmented reality view of the environment. In embodiments,light sources are added to the lighting space model by a user placinglight sources in the augmented reality view of the environment byselecting a model of a light source from a catalog of light sourcespresented in the augmented reality environment and indicating at leastone of a position and orientation of the light source in theenvironment; rendering the environment through the augmented realitydisplay including lighting effects of the placed light sources based ona near field illumination characterization of the placed light sources;and populating a data object with item identification information for atleast one of the placed light sources. In embodiments, populating causesautomatic placement of at least one order into a supply chain for the atleast one placed light source.

In embodiments, the rendering includes rendering near-field lightingeffects in the environment of the placed light sources based on anear-file illumination model of the light source.

In embodiments, populating includes obtaining the item identificationinformation from the catalog of light sources. In embodiments,populating is in response to a user indicating in the augmented realityinterface the at least one of the placed lights for automatic orderplacement. In embodiments, the methods include producing a lightinginstallation plan based on the floor plan and the position andorientation of the light source in the environment.

In embodiments, the method includes populating an automatic procurementdata object with item identification information for at least one objectidentified in the floor plan. In embodiments, the rendering includesaccounting for at least one of light transparency, absorption andreflection of at least one element in the environment. In embodiments,the rendering includes rendering near-field lighting artifacts. Inembodiments, the near-field lighting artifacts are rendered throughoutthe three-dimensional space. In embodiments, the rendering includesaccounting for effects relating to physical characteristics of at leastone light source.

In embodiments, the at least one light source includes a plurality ofdistinct light elements, each distinct light element being associatedwith a corresponding set of ray-traces. In embodiments, the renderingincludes rendering effects from each of the plurality of distinct lightelements. In embodiments, the lighting effects of the placed lightsources are based on an area-source model of the placed light sources.In embodiments, the catalog of light sources includes automated orderlight source identification information. In embodiments, the renderingis performed by a volumetric renderer.

In embodiments, methods and systems for planning lighting in anaugmented reality display, include representing physical features of anenvironment as surfaces and edges; using machine learning to generate alighting space model of the environment from the surfaces and edges. Inembodiments, the lighting space model includes attributes for at leastone of absorption and reflectance of each of the surfaces; using thelighting space model to produce a floor plan of the environment;coupling the lighting space model to an augmented reality view of theenvironment. In embodiments, light sources are added to the lightingspace model and to the produced floor plan by a user placing lightsources in the augmented reality view of the environment; rendering theenvironment through the augmented reality display including lightingeffects of the placed light sources based on a near field illuminationcharacterization of the placed light sources; and populating a dataobject with item identification information for at least one of theplaced light sources. In embodiments, populating causes automaticplacement of at least one order into a supply chain for the at least oneplaced light source.

In embodiments, the rendering includes rendering near-field lightingeffects in the environment of the placed light sources based on anear-file illumination model of the light source. In embodiments,populating includes obtaining the item identification information fromthe catalog of light sources. In embodiments, populating is in responseto a user indicating in the augmented reality interface the at least oneof the placed lights for automatic order placement. In embodiments, themethod includes producing a lighting installation plan based on thefloor plan and the position and orientation of the light source in theenvironment. In embodiments, the method includes populating an automaticprocurement data object with item identification information for atleast one object identified in the floor plan. In embodiments, therendering includes accounting for at least one of light transparency,absorption and reflection of at least one surface in the environment. Inembodiments, the rendering includes rendering near-field lightingartifacts.

In embodiments, the near-field lighting artifacts are renderedthroughout the three-dimensional space. In embodiments, the renderingincludes accounting for effects relating to physical characteristics ofat least one light source. In embodiments, the at least one light sourceincludes a plurality of distinct light elements, each distinct lightelement being associated with a corresponding set of ray-traces. Inembodiments, the rendering includes rendering effects from each of theplurality of distinct light elements. In embodiments, the lightingeffects of the placed light sources are based on an area-source model ofthe placed light sources. In embodiments, the rendering is performed bya volumetric renderer.

In embodiments, methods and systems of control of modeled light sourcesin an augmented reality interface, include coupling a lighting spacemodel of an environment to an augmented reality view of the environment.In embodiments, light sources are added to the lighting space model by auser placing light sources in the augmented reality view of theenvironment; rendering the environment through the augmented realitydisplay including lighting effects of the placed light sources based ona near field illumination characterization of the placed light sources;and configuring a plurality of virtual lighting controls in theaugmented reality user interface that control illumination from at leastone of the placed light sources.

In embodiments, the plurality of virtual lighting controls includes userinterface elements for controlling at least one of dimming level,fixture color, fixture finish, beam angles, light intensity, lightcolor, and light color temperature. In embodiments, the plurality ofvirtual lighting controls includes user interface elements forcontrolling at least one of rotation, placement, orientation, and tiltof the placed light sources. In embodiments, the method includesinterfacing the virtual lighting controls to wearable sensors thatindicate a motion of a portion of a user wearing the wearable sensors.

In embodiments, the indication of motion from the wearable sensors isinterpreted by the plurality of virtual lighting controls to control atleast one of light intensity, light color, and light color temperature.In embodiments, the indication of motion from the wearable sensors isinterpreted by the plurality of virtual lighting controls to control atleast one of rotation and tilt of the placed light sources. Inembodiments, the method includes rendering a marketplace of lightsources in a portion of the augmented reality display from which a userselects the light sources to be added to the lighting space model.

In embodiments, methods and systems of control of modeled light sourcesin an augmented reality interface, include coupling a lighting spacemodel of an environment to an augmented reality view of the environment.In embodiments, light sources are added to the lighting space model by auser placing light sources in the augmented reality view of theenvironment; rendering the environment through the augmented realitydisplay including lighting effects of the placed light sources based ona near field illumination characterization of the placed light sources;and configuring a plurality of virtual lighting controls in a userinterface of a handheld portable computing device that control at leastone of the placed light sources.

In embodiments, the plurality of virtual lighting controls includes userinterface elements for controlling at least one of light intensity,light color, and light color temperature. In embodiments, the pluralityof virtual lighting controls includes user interface elements forcontrolling at least one of rotation and tilt of the placed lightsources. In embodiments, the method includes interfacing the virtuallighting controls to wearable sensors that indicate a motion of aportion of a user wearing the wearable sensors. In embodiments, theindication of motion from the wearable sensors is interpreted by theplurality of virtual lighting controls to control at least one of lightintensity, light color, and light color temperature. In embodiments, theindication of motion from the wearable sensors is interpreted by theplurality of virtual lighting controls to control at least one ofrotation and tilt of the placed light sources.

In embodiments, the method includes rendering a marketplace of lightsources in a portion of the augmented reality display from which a userselects the light sources to be added to the lighting space model. Inembodiments, the virtual lighting controls include a touchable elementon an electronic tablet display. In embodiments, the virtual lightingcontrols include an adjustable dial that represents a range of filtereffects producible by the light. In embodiments, methods and systems ofcontrol of modeled light sources in an augmented reality interface,include coupling a lighting space model of an environment to anaugmented reality view of the environment. In embodiments, light sourcesare added to the lighting space model by a user placing light sources inthe augmented reality view of the environment; rendering the environmentthrough the augmented reality display including lighting effects of theplaced light sources based on a near field illumination characterizationof the placed light sources; and configuring the lighting space model toreceive input from a handheld portable computing device that control atleast one of the placed light sources.

In embodiments, the input from the handheld portable computing deviceincludes data that indicates at least one of an orientation and amovement of the handheld portable computing device. In embodiments, theorientation input indicates a new orientation in the environment for theat least one of the placed light sources. In embodiments, the movementinput indicates a new position in the environment for the at least oneof the placed light sources.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a schematic diagram of the main components of a platform fordesign, fulfillment, deployment, and operation of a lightinginstallation in accordance with the embodiments of the presentdisclosure.

FIG. 2 is a requirements system diagram of a platform for design,fulfillment, deployment, and operation of a lighting installation inaccordance with the embodiments of the present disclosure.

FIG. 3 is a control requirements diagram of a platform for design,fulfillment, deployment, and operation of a lighting installation inaccordance with the embodiments of the present disclosure.

FIG. 4 is a system diagram of the main systems of a platform for design,fulfillment, deployment, and operation of a lighting installation inaccordance with the embodiments of the present disclosure.

FIG. 5 is a deployment environment diagram of a platform for design,fulfillment, deployment, and operation of a lighting installation inaccordance with the embodiments of the present disclosure.

FIG. 6 is a control IT infrastructure diagram of a platform for design,fulfillment, deployment, and operation of a lighting installation inaccordance with the embodiments of the present disclosure.

FIG. 7 is a lighting design environment diagram of a platform fordesign, fulfillment, deployment, and operation of a lightinginstallation in accordance with the embodiments of the presentdisclosure.

FIG. 8 is a lighting space model, knowledge base and lighting objectlibrary diagram of a platform for design, fulfillment, deployment, andoperation of a lighting installation in accordance with the embodimentsof the present disclosure.

FIG. 9 is a diagrammatic view that depicts flow for characterization ofthe near field illumination characteristics of a lighting source orlighting fixture in accordance with the embodiments of the presentdisclosure.

FIG. 10 is a diagrammatic view that depicts a lighting fixture objectmoving along a single axis in an indirect measurement system inaccordance with the embodiments of the present disclosure.

FIG. 11 is a diagrammatic view that illustrates near field metrics thatmay be characterized in a near field measurement system in accordancewith the embodiments of the present disclosure.

FIG. 12 is a diagrammatic view that depicts a user interface of theplatform for a scan process in accordance with the embodiments of thepresent disclosure.

FIG. 13 is a diagrammatic view that depicts a user interface of theplatform for a lighting space model selection process of a lightingdesign environment in accordance with the embodiments of the presentdisclosure.

FIGS. 14, 15, 16, and 17 are diagrammatic views that depict lightingspace model design renderings in accordance with the embodiments of thepresent disclosure.

FIGS. 18 and 19 are diagrammatic views that depict scene renderings fora scene that includes lighting elements in accordance with theembodiments of the present disclosure.

FIGS. 20 and 21 are diagrammatic views that depict a user interface fora lighting fixture object selection in accordance with the embodimentsof the present disclosure.

FIGS. 22 and 23 are diagrammatic views that depict a user interactingwith AR functionality in accordance with the embodiments of the presentdisclosure.

FIGS. 24 and 25 are diagrammatic views that depict a user interactingwith near field functionality in accordance with the embodiments of thepresent disclosure.

FIGS. 26 and 27 are diagrammatic views that depict VR interfaces inaccordance with the embodiments of the present disclosure.

FIGS. 28 and 29 are diagrammatic views that depict a user interface of arequirements system in accordance with the embodiments of the presentdisclosure.

FIGS. 30, 31, and 32 are diagrammatic views that depict a user interfacefor setting filters and emotions of a design in accordance with theembodiments of the present disclosure.

FIG. 33 is a diagrammatic view that illustrates the development of anunderstanding of the features of an environment using a featureextraction system in accordance with the embodiments of the presentdisclosure.

FIG. 34 is a diagrammatic view that illustrates scanning of anenvironment for which a lighting installation is to be designed inaccordance with the embodiments of the present disclosure.

FIG. 35 is a diagrammatic view that illustrates various filters that maybe used to determine control parameters and other characteristics of alighting design for an environment in accordance with the embodiments ofthe present disclosure.

FIG. 36 is a diagrammatic view that illustrates alternative scenes thatmay be produced using aesthetic filters in a lighting design inaccordance with the embodiments of the present disclosure.

FIG. 37 is a diagrammatic view that illustrates communication amongcomponents in a deployed lighting installation in accordance with theembodiments of the present disclosure.

FIG. 38 is a diagrammatic view that illustrates alternative spectraltuning curves that may be enabled, such as for programmable dimming orcolor tuning modes in accordance with the embodiments of the presentdisclosure.

FIGS. 39A, 39B, 39C, 39D, 40, and 41 are diagrammatic views that depictembodiments of spectral tuning of lighting curves for lighting fixtures,as may be specified or selected using an interface of the platform or arelated system in accordance with the embodiments of the presentdisclosure.

FIG. 42 is a diagrammatic view that depicts embodiments of a near-fieldcharacterization system in accordance with the embodiments of thepresent disclosure.

FIG. 43 is a diagrammatic view that depicts an embodiment of volumetricrendering in accordance with the embodiments of the present disclosure.

FIG. 44 is a diagrammatic view that depicts embodiments of legacy colorprogrammable control in accordance with the embodiments of the presentdisclosure.

FIG. 45 is a diagrammatic view that includes a flow diagram that depictsincremental light model generation using an iterative image capture andregenerative algorithm in accordance with the embodiments of the presentdisclosure.

FIG. 46 is a diagrammatic view that includes a flow diagram that depictsincremental image capture and aggregated image processing by aregenerative algorithm that produces a near field volume luminance modelof a light source in accordance with the embodiments of the presentdisclosure.

FIG. 47 is a diagrammatic view that includes a flow diagram that depictsgenerating a candidate set of light sources based on attributes of adesired bloom effect being compared to light source features inaccordance with the embodiments of the present disclosure.

FIG. 48 is a diagrammatic view that includes a flow diagram that depictsgenerating a candidate set of light sources based on a comparison of adesired bloom effect to light bloom effects stored in a library of lightsources in accordance with the embodiments of the present disclosure.

FIG. 49 is a diagrammatic view that depicts embodiments of themulti-device virtual/augmented reality light modeling methods andsystems in accordance with the embodiments of the present disclosure.

DETAILED DESCRIPTION

FIGS. 1 through 8 depict a platform 100 for the design, fulfillment,deployment, and operation of a lighting installation. The platform 100for the design, fulfillment, deployment, and operation of a lightinginstallation may include a lighting design environment 238, a control ITinfrastructure 282 and a deployment environment 284. The deploymentenvironment 284 may include a lighting installation 280. The lightinginstallation 280 may include a lighting installation sensor system 266for sensing and collecting data from the environment of the lightinginstallation 280, using any of a wide range of sensor inputs, includingmotion sensors, temperature sensors, light sensors, flow sensors,chemical sensors, and others.

In embodiments, the lighting installation 280 may connect to orintegrate with one or more scanning system input interfaces 202, inputsources 206 and/or autonomous control systems, either directly, througha network, through a lighting installation sensor system, or the like.In embodiments, an autonomous control system 262 may connect to orintegrate with various input sources 206, a workflow guidance system242, an operational feedback system 264 and/or a control IT interface282. Input sources 206 may also connect to or integrate with and provideinput to the control IT infrastructure 282, the workflow guidance system242 and/or the scanning system input interfaces 202. The workflowguidance system 242 may also connect to or integrate with the control ITinfrastructure 282, the operational feedback system 264, the lightingdesign environment 238, a near field characterization system 270 and/orscanning system output interfaces 204.

In embodiments, the scanning system input interfaces 202 may alsoconnect to or integrate with a scanning system 102. The scanning systeminput interfaces 202 may also connect to or integrate with a scan datastore 208 and/or a scan alignment system 210. The scan data store 208may also connect to or integrate with the scan alignment system 210. Thescan alignment system 210 may connect to or integrate with a machinebased alignment 212 system. Scanning system output interfaces 204 mayalso connect to or integrate with the near field characterization system270, the lighting design environment 238, the operational feedbacksystem 264, the workflow guidance system 242, the control ITinfrastructure 282, the one or more input sources 206 and/or theautonomous control system 262. The near field characterization system270 may also connect to or integrate with the operational feedbacksystem 264, the lighting design environment 238, the workflow guidancesystem 242 and/or the control IT infrastructure 282.

In embodiments, the deployment environment 284 may connect to, include,or be integrated with the control IT infrastructure 282 and the lightingdesign environment 238. The deployment environment 284 may connect to orbe integrated with the control IT infrastructure 282 through theautonomous control system 262, the input sources 206, the workflowguidance system 242, the scanning system output interfaces 204, the nearfield characterization system 270 and/or the operational feedback system264. The deployment environment 284 may, moreover, connect to or beintegrated with the lighting design environment 238, such as through theoperational feedback system 264, the near field characterization system270, the scanning system and output interfaces 204, the workflowguidance system 242, one or more input sources 206 and/or the autonomouscontrol system 262.

FIG. 6 provides an exemplary diagram of the control IT infrastructurefor design, fulfillment, deployment and/or operation of a lightinginstallation. In embodiments, the control IT infrastructure 282 mayinclude an automated fulfillment system 128 and an automated orderingsystem 130. The automated fulfillment system 128 may connect to theautomated ordering system 130, the lighting design environment 238and/or the deployment environment 284 or to the various elements,components, or sub-systems of each of them. The automated orderingsystem 130 may connect to or be integrated with the automatedfulfillment system 128, the lighting design environment 238 and/or thedeployment environment 284.

FIG. 7 provides the lighting design environment 238 diagram of aplatform for design, fulfillment, deployment, and operation of alighting installation. The lighting design environment 238 may include amarketplace API 278 that provides a programmatic interface to one ormore sources of market-relevant information about lighting objects andlighting fixtures, such as specification information, pricinginformation, delivery information and the like. The marketplace API 278may connect to or be integrated with one or more of the control ITinfrastructure 282, a data API 274, a lighting project data structure254, a library of templates 246, a lighting design tool set 286, alighting space model, a knowledge base and lighting object library 214,a light output modeling system 288, a visual representation generationengine 240, a budgeting system 248 and/or an analytics module 290. Theanalytics module 290 may also connect to or integrate with the lightingproject data structure 254, the library of templates 246, the lightingdesign tool set 286, the lighting space model, the knowledge base andlighting object library 214, the light output modeling system 288, thevisual representation generation engine 240 and/or the budgeting system248.

In embodiments, the platform 100 for the design, fulfillment,deployment, and operation of a lighting installation, referred to as theplatform 100, may include or be integrated with various systems,components, features, methods, sub-systems, and the like for enablingthe lighting marketplace. The lighting marketplace may include thelighting marketplace API 278, as discussed elsewhere in this disclosure.The lighting marketplace may support real-time pricing and the variousfinancial models that exist within the lighting design industry, such asproject-based models, per-fixture models, cost-of-light models,cost-plus models, design-build models, fee-for-service models, andothers.

The lighting marketplace may include a recommendation engine 122. Therecommendation engine 122 may be integrated within the lighting designenvironment 238. The recommendation engine 122 may receive constraintsfrom a project. The constraints from the project may be known whenlooking for lighting fixture objects 230 and may include pricingconstraints (such as relating to an overall project budget or a budgetfor a particular item or line item within a budget), timing constraints(such as relating to when a project or portion of a project is supposedto be completed) and the like. The constraints of the project may alsobe specified in requirements system 106. The recommendation engine 122may receive a complete lighting design (or portions thereof) and maysuggest lighting fixture object 230 substitutions to support, forexample, a value engineering design approach.

In embodiments, Augmented Reality and Virtual Reality (AR/VR)functionality may be integrated as a front end to a design stage andprovide input to the lighting marketplace. For example, a user mayexperience the lighting design environment in a VR environment, such asseeing a virtual reality representation of a proposed lightinginstallation (including seeing fixtures and seeing illumination effectsrendered in a 3D realistic environment) or an AR environment (such asseeing a room or other environment for which a lighting design isproposed, with overlays that represent lighting objects, fixtures or thelike, as well as illumination effects. In embodiments, AR/VRfunctionality may obtain information from or provide input to thelighting marketplace of the platform 100 through the marketplace API278. For example, the AR/VR system may allow a user to select items thatare available for sale (optionally constrained to ones that fit withinbudgets and timelines for a project) and have those items represented inthe AR/VR environment. Users may drag, drop, position, orient and movelighting objects to view the objects and their illumination effects,such as using gestures or speech as inputs to the AR/VR system.

The lighting marketplace may include support for product registration.Product registration may include providing enhanced support, such asaccessing information about when a product was ordered, arrived, wasdelivered and the like. The marketplace API 278 may include a data feedinto the lighting marketplace and provide benchmarking data regardingactual delivery dates for items over time. This information may allowthe lighting marketplace to predict delivery times based on orderhistory.

In embodiments, the lighting marketplace may include data collectionelements that are not readily accessible in a standard B2B environment.

In embodiments, the lighting marketplace may deal with commercialfreight while providing consumer-focused e-commerce features. Forexample, the lighting marketplace may allow customers to rate productsonce they have used the product. In embodiments, the lightingmarketplace may include back-end data gathering for benchmarkingsupplier performance, such as benchmarking returns and delivery times.In embodiments, the lighting marketplace API 278 may provide a highlevel of data integration with a design process, by providing timingparameters, prices, ratings, lighting source objects 228, projectrequirements and the like as elements of or filters to the designprocess, such affecting what is viewed by a user of a platform in thedesign environment, in a dashboard for managing an installation, or thelike.

By enabling rich visualizations, the platform allows lighting designersand lighting vendors to show the benefits of more expensive designfeatures that are high impact in the design, potentially increasingsales of such features. Thus, the platform, by showing such benefits,may enable vendors and designers to sell certain aspects of the designthat might have been omitted absent the visualization features of theplatform 100. The term “designer” as used herein encompasses, exceptwhere context indicates otherwise, lighting designers, architects,interior designers, building contractors, and other individuals involvedin designing, installing, operating and/or maintaining lightinginstallations.

In embodiments, the lighting marketplace may be integrated with thelighting design environment 238. The lighting marketplace may be builtinto the lighting design environment 238 so that if a user sets designtargets and budget targets in a way that, if they cannot achieve thedesign targets in budget, the platform may recommend components of thelighting design environment 238 that purposely conflict with suchconstraints, rather than simply filtering components out of the lightingdesign environment 238 and coming out with “null” values. Instead, theplatform may come back to the user and indicate that the user mayachieve the effect with a larger budget as the primary answer, forexample, rather than a “null” value if there are conflictingrequirements, such as design and budget requirements. Providingrecommended alternative components may encourage users toward higherquality designs, rather than just having them seek low-cost options thatsatisfy an original budget.

In embodiments, the lighting marketplace may be integrated with anInternet of Things (IoT) system, allowing a user to take advantage of anunderstanding made available by an IoT system, such as an understandingof how a lighting installation is used, helping a user of the platform100, such as a designer, better understand the use of a space. Forexample, if customers in a restaurant are going to tables with aspotlight, then a platform may recommend ordering additional spotlights.In embodiments, the IoT integration may also allow the lightingmarketplace to track users, by providing user tracking data as an inputto the lighting marketplace, such as recommending an upgrade to thedesign. In these examples, IoT integration with the lighting marketplacemay allow a user of a platform to gauge the impact of lighting on whatis happening in a store, restaurant, and other locations.

In embodiments, the lighting marketplace may provide an enhanced senseof spatial awareness to a user, by extracting data for determining otherelements of what is happening in a space. For example, for a retail userwith a merchandising plan and a store, the platform 100 may allow theretail user to compare patterns and dwell times in the store, to gaugethe effectiveness of influence on buying behavior of what is takingplace in the store. In embodiments, one of the elements of the platform100 that may interact with design, marketplace and implementation is anunderstanding of the lighting design environments 238 that products arebeing deployed in, allowing the user of the platform 100 to build adatabase of types of lighting design environments 238, regions of thecountry and the like, such as regarding how people are deploying andusing light in predetermined regions or environments.

In embodiments, this understanding may indicate things like: “People inthe Northwest like cooler color temperatures and downlights” or “Sushirestaurants deploy lighting like this.” Because the lighting marketplaceincludes data on the type of space and what the space was designed for,the platform 100 may provide a user with an understanding of whatlighting fixture objects 230 and deployments are typical for thelighting design environment 238.

In embodiments, lighting objects that include dynamic color tuning anddynamic lighting distributions may communicate with the platform 100,such as via two-way digital communication. This may include interactionswith marketplace features of the platform 100. In these examples, if thelighting marketplace is aware of ten different pre-set ways for users tochange the lighting distribution of the lighting design environment 238,the lighting marketplace may also be aware of what lighting distributionis most often chosen for that lighting design environment 238 andindicate that lighting distribution as the most preferred or most oftenimplemented lighting distribution for that specific design environment.Also, such dynamic color and distribution tuning capabilities may berepresented in the design environment, including in AR and VRinterfaces, so that a user may see how a lighting object may be changedto create different effects in a given installation.

In embodiments, tuning information may be sent to the recommendationengine 122 and stored for future use. By way of these examples, a usermay choose the preferred lighting distribution for the lighting designenvironment 238 in which the user is deploying. This information mayindicate where and when users are using dynamic color, such as forlighting particular types of merchandise that appear more favorablyunder particular color tuning parameters. In embodiments, thisinformation may also indicate other choices being made by users. Inembodiments, the information may be collected over time by tracking thebehavior of users of the platform 100 and using it in the recommendationengine 122 on the design side and in the lighting marketplace componentsof the platform 100.

In embodiments, the IoT integration with the platform 100 may includebuilding integration. By way of these examples, the IoT buildingintegration may include tracking people and understanding their dwelltimes, which may be used by a platform for purposes of occupancysensing, space scheduling, and space optimization, among others. Infurther examples, the platform 100 may be integrated with a responsiveHVAC system, providing health benefits to the lighting designenvironment 238. In these examples, the IoT integration may includeambient temperature, oxygen, and carbon dioxide sensing.

In embodiments, the IoT integration may include dynamic color andspectrum lighting sensing and detection. By way of these examples,dynamic color and spectrum lighting sensing and detection may indicatedifferent access to daylight, versus not having daylight in a lightingenvironment. Dynamic color and spectrum lighting sensing and detectionmay also indicate different spectral content (such as during a sunny orcloudy day) and what ambient light is coming through to the lightingenvironment.

In embodiments, the IoT integration may provide a platform with accessto elements of connected devices that may feed back into cloud-basedsystems, that may be responsive to people, such as providing biometricdata from wearable devices, social media feeds, including Twitter™feeds, Facebook™ posts, Pandora™ streams and the like. In theseexamples, this integration may allow a user of a platform to assess anduse lighting as a method for altering mood, productivity, or the likebased on non-lighting, building inputs.

In embodiments, a hotel may have spectrally dynamic, tunable lightingintegrated with its reservation systems. By way of these examples, thereservation system may have a desired profile learned or programmed sothat when a guest checks into the hotel, the user preferences, such asspectral content, for the hotel or hotel room are set. In furtherexamples, a business traveler may be checking into a hotel having movedamong a significant number of time zones. Using information from thetraveler's calendar, including where the traveler has been historically,the platform 100 having the IoT integration may be able to adjustspectral control to manipulate the traveler's body clock, so thetraveler is better able to wake and function at the times the travelerneeds to be able to do that relative to time zone changes and travel.Spectral content may include visible light and other settings thetraveler may not be aware of, based on the traveler's schedule, forexample. Furthermore, the traveler could opt into being helped withtheir sleep patterns in this manner.

In embodiments, a restaurant may have down lights at tables. The tablesmay be moved, for example, to accommodate different group sizes. IoTintegration with a platform could, when the operator of the restaurantmoves the table, rearrange the down lights when the tables are moved, soa spotlight is shining on each table, even after a table is moved. Inembodiments, dynamically applied ambient lighting and spot lighting maybe controlled by IoT platform integration, so that, for example, when alarge party comes enters a restaurant, spotlights shine on occupiedtables, rather than the empty tables. In embodiments, when a table needsto be cleared (such as in a user interface that shows the environmentand objects in it), the table may be tagged or otherwise prioritized inthe lighting distribution map for the restaurant, such as to highlightit for workflow purposes, and the lighting of the table may be alteredto highlight it (such as showing it with different color), such as tothe staff person responsible for clearing tables. In embodiments, one ormore of the lighting source objects 228, such as a spotlight, may bedimmed when not in use, saving energy and not highlighting the existenceof empty tables.

In embodiments, filtered glasses and predetermined adjustment andreadjustment of the gamut area in a particular space may be used toenable functionality where, without the filtered glasses, correlatedcolor temperature (CCT), luminosity, or any of the chromaticities in thegamut area may appear to stay constant, but with the filtered glassesany of the changes become apparent. This may allow interestingsituations in hotels and restaurants. In these examples, hotel andrestaurant staff wearing the filtered glasses could be directed in avery subtle manner because the filtered glasses may allow the user toperceive the changes in the chromaticities of gamut area, luminosity,correlated color temperature (CCT), and the like while the customers aretotally unaware (or at least sufficiently unaware) of the staff beingdirected.

In embodiments, a lighting project data structure 254 may also connectto or integrate with one or more of the data API 274, the library oftemplates 246, the lighting design tool set 286, the lighting spacemodel, the knowledge base and lighting object library 214, the lightoutput modeling system 288, the visual representation generation engine240, and/or the budgeting system 248. The library of templates 246 mayalso connect to one or more of a collaboration system 244, acustomization API 276, the lighting design tool set 286, the lightingspace model, the knowledge base and lighting object library 214, thelight output modeling system 288, the visual representation generationengine 240, and/or the budgeting system 248. The lighting design toolset 286 may connect to or integrate with one or more of the lightingspace model, the knowledge base and lighting object library 214, thelight output modeling system 288, the visual representation generationengine 240, the budgeting system 248, the collaboration system 244and/or the customization API 276.

In embodiments, the lighting space model, the knowledge base, and thelighting object library 214 may also connect to or integrate with one ormore of the collaboration system 244, the customization API 276, thelight output modeling system 288, visual representation generationengine 240, and/or the budgeting system 248. The light output modelingsystem 288 may also connect to or integrate with one or more of thecollaboration system 244, the customization API 276, the visualrepresentation generation engine 240 and/or the budgeting system 248.The visual representation generation engine 240 may also connect to orintegrate with one or more of the collaboration system 244, thecustomization API 276 and/or the budgeting system 248. The budgetingsystem 248 may also connect to or integrate with the collaborationsystem 244 and/or the customization API 276.

FIG. 8 illustrates the lighting space model, the knowledge base, and thelighting object library 214 of the platform for design, fulfillment,deployment, and operation of a lighting installation. The lighting spacemodel, the knowledge base, and the lighting object library 214 mayinclude, connect to, or integrate with a lighting schedule system 258,an automated search system 234 and/or a feature extraction system 218. Alighting schedule system 258 may connect to one or more of a lightingschedule 256, the automated search system 234, a lighting object library232, the feature extraction system 218 and/or a light source test system272. The automated search system 234 may also connect to or integratewith one or more of the lighting schedules 256, the lighting objectlibrary 232, the feature extraction system 218 and/or a light sourcetest system 272. The feature extraction system 218 may also connect toor integrate with one or more of the lighting schedule 256, the lightingobject library 232 and/or the light source test system 272. The lightingschedule may also connect to or integrate with one or more of thelighting object library 232, the machine based lighting augmentationsystem 216 and/or the light source test system 272. In embodiments, thelight source test system 272 may also connect to one or more lightingsource objects 228. The machine based lighting augmentation system 216may also connect to the lighting space knowledge base 222. The lightingobject library 232 may also connect to or integrate with one or more ofthe lighting space knowledge base 222, one or more lighting objects 226,a crowdsourcing system 224 and/or a manufacturer and a product feedbackinterface 236. The lighting space knowledge base 222 may also connect toor integrate with the crowdsourcing system 224. The lighting objects 226may also connect to or integrate with one or more of the lighting sourceobjects 228, lighting fixture objects 230 and/or lighting space objects220. The lighting space objects 220 may also connect to or integratewith one or more of a space utilization data structure 268. Thecrowdsourcing system 224 may include a crowdsourcing interface 252.

In embodiments, the platform 100 includes systems that enable efficientscanning of a space in which the lighting installation 280 is desired;automated conversion of a scan into a model or representation of thespace that is displayed to a designer; specification 144 of variousrequirements for the space (including financial parameters, functionalrequirements 112 and aesthetic requirements 116, including relating tothe desired emotional impact of the installation); automated search of auniversal library of lighting products and filtering to a set thatsatisfies the requirements; automated generation of recommendations(including based on various filters, including emotive filters,collaborative filters, and others); manipulation of the model orrepresentation to show the impact of various lighting products underdifferent parameters or conditions; automated generation of a manifestor order for the lighting products required for the installation,automated ordering and fulfillment of the installation; remote operationand maintenance of the installation; and autonomous control of theinstallation (including self-commissioning and automated self-adjustmentof the installation and including peer-to-peer and other configurationsfor control within the installation).

In embodiments, the platform may include methods, systems, modules,components and devices for scanning a space where the lightinginstallation 280 is to be deployed, all of which are collectivelyreferred to as the “scanning system” 102. FIG. 34 illustrates userscanning 660 of an environment 662 for which a lighting installation isto be designed, such as using a mobile or handheld scanning device 664.In embodiments, the scanning system 102 may operate with various inputsources 206, which may be loaded by one or more scanning systeminterfaces. Scanning system interfaces may include scanning system inputinterfaces 202 and scanning system output interfaces 204, which inembodiments may include a single, integrated interface. In embodiments,the input sources 206 may include images and video of a space capturedby one or more cameras, spatial or point cloud scans conducted usinginfrared, acoustic, sonic or laser scanners (among others), scans usingvarious types of sensors deployed on mobile units, such as drones androbots, floor plans, blueprints, drawings (which may be converted frompaper form into design models or may exist in models, such ascomputer-aided design (CAD) models), and other sources. In embodiments,the scanning system interfaces may include application programminginterfaces (such as allowing automated feeding of information into thescanning system 102 from another system) and various other types ofinterfaces used for extraction, transformation and loading of data ormigration of data into the scanning system 102, including brokers,connectors, bridges, gateways, and the like, as well as data marts, datawarehouses, micro marts, and other facilities for intermediate handlingof data. In embodiments, the data from various input sources 206 may benormalized, such as by automatically converting it into a common format(such as a relational database format, an XML format, or the desiredfile format), so that it may be loaded into a scan data store 208 (whichmay be a distributed database in the cloud). In embodiments, informationfrom various scanning input sources 206 may be aligned in the scanalignment system 210, such as by scaling representations from differentsources (e.g., a CAD model and a point cloud scan) to a common spatialframe of reference. This alignment may include machine-based alignment212 of the scans based on the automated extraction of features from thescans.

Lighting designers and architects may be required to survey an existingspace, take measurements, and draw out a floor plan before they maybegin to lay out a lighting design. To help accelerate and automate thisprocess, the platform 100 may generate floor plans for existing spaces.In embodiments, the generating of floor plans for existing spaces mayintegrate with hardware and software such as the Matterport™ camera,Structure 3D™ sensor coupled with a tablet, Apple's ARKit™ on an iPhone™or iPad™ wireless computing devices and the like, to scan an existingspace and generate its point cloud representation. In embodiments, theMachine learning algorithms may then be used to process these floorplans in the form of point clouds and may detect and recognize thevarious structural/architectural elements and features in the space. Byway of these examples, the machine learning may, in turn, construct alighting space model 214 of the space. Such a system may output a floorplan or reflected ceiling plan as necessary. Alternatively, the systemmay directly process a point cloud to produce a floor plan.

In embodiments, if using a technology like ARkit™ by Apple™, thelighting schedule 256 may be added on top of floor plan generated by theplatform 100. The platform 100 may allow a lighting designer to placelighting fixture objects 230 in the lighting space model 214 through anaugmented reality (AR) interface as the lighting designer scans thelighting design environment 238. In embodiments, the lighting designenvironment 238, when viewed through an AR lens, may show the lightingfixture objects 230 and the lighting effects of the lighting fixtureobjects 230 as well. In the AR context, these lighting fixture objectsmay be permanent for the duration of a design session, unless removed orchanged.

In embodiments, a generated floor plan may be registered to a real-worldspace, so lighting fixture objects placed in an AR interface and viewmay be correctly positioned on the floor plan without requiringadditional input from a user. This may allow a lighting designer tocreate a lighting space model 214 on the go and visualize it in thelighting design environment 238. Once complete, the platform 100 maysave a floor plan with the lighting fixture object 230 positions andnote other fixture details to create the lighting schedule 256.

FIG. 13 depicts a user interface of the platform for a scan process inaccordance with embodiments of the present disclosure. A user interfacefor a scan process may include one or more of a scan initiation screen452, scan progress screens 454, 458 and a scan output screen 460. Inembodiments, the scan output screen 460 may present a first imagerendering of a scanned space.

In embodiments, the scanning system 102 may populate or include one ormore lighting space models 214, as depicted in FIG. 8 , which may bebased on features extracted from one or more input sources 206. By wayof these examples, the lighting space models 214 depicted in FIG. 8 maybe augmented (including automated, machine-based lighting modelaugmentation), such as based on an understanding of typicalcharacteristics and parameters of buildings and interiors. In theseexamples, doorways, windows, columns, pillars, paints, carpets,wallpapers, hangings, objects of art, furnishings, fabrics, fixtures,and other elements of a space may be recognized by the featureextraction system 218 that may assign tags, labels, or other properties.Examples of such understanding for an interior office environment 650 (aconference room) are illustrated in FIG. 33 . In embodiments, the modelmay thus store various lighting space objects 220 (which may berepresented and stored in defined classes, with defined properties,attributes, fields, and parameters, such as in an object orientedprogramming languages like Java or C++), and each object may be assignedvarious parameters that are based on actual measurements (such as fromthe feature extraction system 218 or from a scan), may be based oninferences (such as populating the objects with common characteristicsthat are inferred from a lighting space knowledge base 222), or may beentered by a user, such as through a user interface. In embodiments, thefeature extraction system 218 may include a self-learning,self-improving system that is capable of handling 2D and/or 3D spatialdata (such as scan data, point clouds, CAD models, and others) and mayuse machine learning and artificial intelligence-based algorithms toextract features. In embodiments, the self-learning and self-improvementmay occur under human supervision and/or via feedback (such as from anyother system that contains information about the type or characteristicsof a feature, such as an architectural or design model or blueprint),such as by verifying outcomes over multiple operations of the featureextraction system 218. In embodiments, the lighting space objects 220may have various properties, such as dimensions, shapes, colors,reflecting characteristics, absorbing characteristics, surfacecharacteristics, functional characteristics, and many others. Among manyproperties, ones that relate to interaction with light (such astransparency, reflectivity, opacity, surface characteristics, color, andthe like) may be captured and characterized with sufficient detail toallow realistic rendering of the appearance of the lighting spaceobjects 220 under different illumination conditions (includingdirection, intensity, color, color temperature, beam shape, and thelike). In embodiments, object recognition technologies may be used toidentify and characterize the lighting space objects 220, such as bytype and by characteristics, such as specularity, diffusivity,reflectance, glare, and the like. In embodiments, machine vision andobject recognition technologies may be used, such as to identify objectsin photographs, videos or other scans. This capability may use and beimproved by machine learning systems. The lighting space objects 220 mayalso include natural light sources, such as coming from windows. Inembodiments, the lighting space knowledge base 222 may be constructedbased on a series of scans, as well as by soliciting input fromindividuals, such as through the crowd-sourcing system 224 by whichinformation about spaces may be solicited from individuals whoexperience or occupy spaces in general or the particular spaces that areto be modeled.

In embodiments, the platform 100 may use a vector field capture (VFC)system to capture and render the characteristics of light from alighting object or fixture. Vector field capture is a 3D graphicsrendering technique using volumetric data to realistically rendereffects, such as the lighting effects from the lighting fixture. WithVFC, subtle three-dimensional detail may be accurately reproduced, andreal luminaire light characteristics are accurately captured andrendered, such as in the lighting design environment (including, whereapplicable in VR and AR interfaces) using 3D graphics. Among otherbenefits, VFC may render the complete light-volume including subtlenear-field artifacts throughout a three-dimensional space. Inembodiments, the effects relating to the physical characteristics of alighting fixture (such as light-clipping effects from the housing and/orlens assembly of a fixture) may be accurately reproduced. Inembodiments, the VFC may also render the visual effects generated bymultiple point light sources (e.g., LEDs) that may be contained in afixture. It will be appreciated in light of the disclosure thatconventional light rendering techniques typically measure a lightingobject's intensity at various angles at a distance long enough, wherethe lighting object may be treated as a single point source, while VFCaccounts for multiple point-sources effectively allowing for area andvolume light sources. As a result, VFC may be shown to be more accuratethan data from conventional IES representations of light sources orobjects. In embodiments, using VFC, soft shadows are accuratelyrecreated, such as rendering situations where real-world luminaires,especially LED-based luminaires that have multiple point sources thattend to have softer transitions between light and shadow. In many cases,the presence of multiple light origination sources may create complexinteractions of shadows and lit areas, which cannot be reproduced whenonly a single point source is assumed, such as in the conventional IESspecification of a lighting object. Using the VFC system, the intensityfall off of light over distance may be accurately captured and rendered,because intensity fall off may be explicitly included as part of thedataset. In contrast, conventional IES files do not capture falloffparameters. Also, in the VFC system, objects intersecting the lightsource (such as gobos, masks, filters, and physical objects like walls,coves, and the like) may be accurately illuminated. In embodiments, thelight intensity may be calculated as a set of ray-traces from each lightsource to the intersecting element, and the light transparency,absorption, and reflection characteristics of each the intersectingobject may be accounted for, such as to model intensity and color ofeach ray (including transmitted and reflected rays) in the overall 3Drendering environment.

In embodiments, the VFC may capture and reproduce the lightingdistribution from a lighting object in various ways, such as thefollowing. In embodiments, the light volume-data capture process may useeither synthetically-rendered graphic image slices (such as from ray-setdata) or may use photographs captured by photographing cross-sectionalslices of the light from a lighting object, such as a luminaire. Inembodiments, the captured illumination data is processed and thenrendered as a three-dimensional volume. By way of these examples,interpolation is used to reconstruct the light-volume between knownsample points for intensity. In embodiments, the three-dimensionalintersections between the light-volume and illuminated geometry may behandled by z-depth comparisons from a “plane of projection.” In theseexamples, arbitrarily complex 3D objects may be illuminated correctly(including concave objects) because the three-dimensional interpolationemanates from the light source(s) projection point(s). In embodiments,the rendering may be implemented using graphics-card shader-code, suchas to enable high frame-rates. In these examples, the shader-codeenables real-time rendering on platforms with a programmable GPU.Complex geometry may be illuminated with constant frame-rateperformance. Rendering may integrate with other graphics techniques,such as one or more of bump, normal, displacement mapping, surfacematerial properties, one or more bidirectional reflectance distributionfunctions (BRDF), and others. Shadows are accurately rendered forobjects intersecting the light-volume.

In embodiments, the light illumination details may be accuratelyrendered across 3D object surfaces, such as objects represented in thelighting design environment. Subtle details and artifacts, such ascaused by the lens/reflector assembly of a lighting fixture, may bereproduced in three dimensions. Most conventional 3D light renderingtechniques are inaccurate even for only two dimensions (across a planefor example—they do not capture the artifacts present in a real lightsource), while VFC is accurate in three dimensions. Also, existinglight-data descriptions, such as IES files, only capture the lightluminance and angle information as if the light were a single-pointsource, while VFC may accurately render multi-point lighting objects,like LED arrays.

In embodiments, the platform 100 may use VFC-based rendering for variouspurposes. Embodiments include using VFC for photorealistic rendering.Photorealistic rendering applications such as 3DS Max™, Maya™, andBlender™ use ray-tracing to create photorealistic scene-renders;however, the light definition typically used is limited to a “singlepoint source” definition. The standard light definition is the position,direction, intensity, falloff (assumed by model to be linear, quadratic,or the like). Using a VFC definition, these ray-traced lights becomemultiple point sources, with intensity and direction. In embodiments,the platform 100 may provide plugin support for these rendering packages(such as for reading VFC light definitions and rendering) to enablehigh-end rendering packages to more accurately render real-worldluminaries, such as for architectural walkthroughs, movie and television‘virtual sets’, etc.

In embodiments, lighting designers using the platform 100 need a way toexamine luminaire characteristics. Usually, this is done by physicallyhandling the luminaire and testing its output. Given the number ofoptions for a luminaire (output intensity, light angle, light Kelvintemperature, etc.), manually examining one luminaire at a time is verylimiting. Among other things, it requires a large inventory of sampleluminaires. A better option is to use a software capability of theplatform 100 (optionally with an interface on a smartphone, tablet, PC,or Mac) that integrates the VFC technology to realistically simulate aluminaire's light output. Using the software capability, numerousluminaire options may be experimented with and visualized in thelighting design environment. While the lighting object is moved aroundin a virtual environment, options such as beam angle, color temperature,and the like may be quickly adjusted and accurately rendered. Byinteractively trying different light options, and seeing themrealistically rendered, a lighting designer will be better informedabout lighting objects being considered for an installation.

In embodiments, VFC rendering of lighting objects may be integrated intoany software application where real-world subtle characteristics oflighting fixture output are required to be viewed. For example, bycreating the library of existing lighting objects (from numerousmanufacturers), the dominant luminaire model choices may be catalogedand viewed within an application. This lends itself well to building a“virtual catalog” (optionally associated with the lighting marketplaceelements of the platform 100), where the end-user may narrow choicesdown by using either standard “filter options” or by using machinelearning to suggest possible luminaire choices for specificenvironments. The end-user may then visually see the options from alarge set of possible choices in the lighting design environment withinminutes.

In embodiments, VFC rendering allows any lighting designer or softwaredeveloper to accurately render outputs from a lighting object. Providinga VFC-based rendering pipeline for lighting objects may be used toenable an “open architecture” for software development, such asinvolving a light-volume capture tool that creates image slices from alighting object. In embodiments, the resulting dataset may be downloadedfrom, for example, a luminaire manufacturer; a software application toprocess the image slices into one or more data files (in embodimentssuch a data file may be provided by luminaire manufacturer); and arendering plugin that may be added to the rendering pipeline as a“material” applied to the rendered objects. These objects may be used inthe platform 100 enabling representation of a wide range of objects frommany manufacturers.

Among other benefits, VFC rendering reproduces light ray information asindividual source vectors. In embodiments, the light source-position anddirection is accounted for, and subtle light effects based on multiplesource-origins such as crossing shadows (and shadows within the lightvolume) are reproduced. When creating 3D rendered synthetic images,whether for ‘architectural walkthroughs’, movies, television,advertising, video games etc., the images are more photo-realisticallyaccurate than using conventional information like IES files, because the“current best approximation” of an IES file only represents the light asa single point source viewed from a distance. The conventional IESmeasurement cannot account for internal volume details. Also, lightgobos, masks, filters and the like may only be roughly approximated whenassuming a single point source; for example, a LED luminaire thatincludes multiple LEDs along a narrow strip will not accurately show thelight masking that results when the luminaire end is abutting a wallcove. In such a situation, the LEDs on the end will be completelyobstructed but the LEDs toward the middle will be unobstructed, but aconventional rendering based on the IES specification (which assumes asingle point source) cannot account for cases like these.

It will be appreciated in light of the disclosure that VFC renderingtypically requires a larger data set definition than otherlight-rendering techniques (such as IES based rendering). Inembodiments, the VFC data set may be compressed using variouscompression techniques. In embodiments, VFC may be used to account fordirect lighting and indirect-lighting (bounced light). In embodiments,the platform 100 may include various facilities and systems for machinelearning and analytics. In embodiments, the machine learning andanalytics may include learning for design decisions, to find fixtures,to characterize fixtures, to price fixtures, to recommend fixtures andto tune them. In embodiments, the tuning under machine learning mayinclude tuning of colors, color temperatures, timing, lightingdistributions, intensities, beam angles and the like. Tuning may alsoinclude tuning emotional or aesthetic filters and the like.

In embodiments, the machine learning and analytics may be integratedwith the recommendation engine 122. Designers today are used tosearching through lighting fixture objects 230 in a very basic way, suchas searching by voltage, dimming technology and the like. This type ofsearching includes very little, if any, comprehension of what thedesigner actually wants. In embodiments, the machine learning andanalytics may allow a designer using a platform to search within adesign context; for example, the platform 100 may recognize that thedesigner is trying to highlight a walkway through a space, for example,and may start to recommend suitable lighting fixture objects 230 andlight space models 214.

In embodiments, the platform 100 may access and use information aboutthe technical capabilities of lighting fixtures and lighting objects,including capabilities for custom color tuning, custom dimming curves,and custom lighting distribution effects, collectively referred toherein as a custom tuning system. By way of these examples, the customtuning system may include multiple channels, providing a platform userwith many ways to achieve a certain color and intensity. In embodiments,the custom tuning system may sit between a user choosing color andintensity levels and the drivers setting chosen color and intensitylevels in a given lighting object or fixture in the lighting designenvironment 238.

In embodiments, a custom tuning system may include various tuning modes.By way of these examples, tuning modes may be user-selectable tuningmodes. In these examples, a user may indicate to a custom tuning system,by selecting a tuning mode, a bias in terms of how the system is goingto choose to make a color indicated by the user. In embodiments, thebias may include luminosity, efficacy, color quality, such as best coloron metrics, intensity, such as maximum or minimum melanopic flux ratios,and the like.

If a user selects a tuning mode, there are many methods to analyze andmanage information related to a bias. In most instances, users chooseand set a single method. Unless the user is dynamically adjusting thetuning mode, the user has typically picked a given set of parameters foradjusting channels, and that set flows through to control of a lightingobject. Instead, a custom tuning system may allow a user to optimize fora preference and have the biases in the system configured to implementthe preference in the lighting design environment 238. This may include,for example, having the light move through a selected curve of lightingcontrol parameters as the light is dimmed.

Typically, efficacy, color quality, and intensity may be preselected atthe software or firmware level and not otherwise selectable orchangeable by a user. For example, a user may choose a 2700K CCT settingand the platform 100 may use its predetermined bias to achieve thecorrect output according to the selected 2700K CCT setting. The userselectable tuning modes, such as user selectable tuning modes that maybe available to a user in the platform 100, may allow the platform 100,either at the software or firmware level, to dynamically andalgorithmically determine the correct balance of four color channels,based on the user selectable tuning modes available to the user. Eachmode may have an associated algorithm. When a mode is selected by auser, the platform 100 may engage the algorithm associated with theselected mode. In embodiments, the platform 100 may engage the algorithmin a way that is transparent to the user and does not require the userto have an awareness of the underlying algorithm.

In embodiments, a custom tuning system may include a warm dim settingthat covers color points. A warm dim setting may include red, green,blue, and white (RGBW) elements. By way of these examples, the warm dimsetting may include programmable curves and a user interface to manageprogrammable curves. In embodiments, the programmable curve may includea start point, an end point, and a configurable path between the two.The programmable curve may be programmed to provide control based oninput from a 0-10 volt dimming system, a DALI control system, or thelike. In embodiments, the custom tuning system may allow a platform todynamically reassign lighting direction and its intensity at particularpoints, readjusting where light is directed and tuning the spectralcharacteristics of the light, such as efficacy, luminosity, colorquality and melanopic flux.

In embodiments, dynamically reassigning lighting direction and intensityusing a custom tuning system may include a capability to readjust wherethe light goes and to adjust the content of the light to make itresponsive to the lighting design environment 238. This may effectivelydisaggregate the resulting light distribution or other illuminationeffects from a particular light fixture object 230, which also maydisaggregate a lighting experience from installed lighting fixtureobjects 230. In embodiments, this disaggregation (created by making anygiven lighting fixture object 230 much more flexible) in turn providesmuch better flexibility in the design of a lighting installation becausemany more lighting fixture objects 230 may satisfy the designconstraints of a given installation.

In embodiments, the custom tuning system may connect lighting variablesto filters, such as aesthetic filters; for example, it may be observedthat for a given application, like lighting a desktop used for detailedwork, maximizing luminosity is a key parameter for a “desktop filter.”For a filter that intends to create a given mood (e.g., a “romanticfilter”), parameters may be tuned to provide warm color temperatures anda palette of colors that are perceived as being more romantic. Inembodiments, connections between lighting control parameters for acustom tuning system and one or more filters may be learned through themachine learning capability of a platform, such that over time thesystem recommends and enables the design, purchasing (such as throughthe lighting marketplace) and control of custom tuned lighting objectsand fixtures that satisfy desired emotional and aestheticcharacteristics as represented by filters selected by a user.

In these examples, connections among lighting variables and filters mayallow a platform to tune to different scenes, such as lunch, cocktailhour, or dinner scenes, translating the filters to the variables. Inembodiments, the connections may create an abstraction layer between thetechnology that enables a desired filter or effect and the intent of theuser. By way of these examples, the user's intent (which may be embodiedin selecting a filter) may include keeping people alert, making peoplefeel happy, supporting romance and the like. In embodiments, theplatform 100, under machine learning, may learn to embody that intent byvariation, selection and promotion of custom tuning parameters forlighting objects and fixtures, based on feedback from users as towhether a given object, fixture or installation achieves the desiredintent.

A lighting space model 214 may also include one or more of the lightingobjects 226, including the lighting source objects 228 and lightingfixture objects 230. In embodiments, the lighting source objects 228 andthe lighting fixture objects 230 may include novel data structures,which may be represented and stored in defined classes, with definedproperties, attributes, fields and parameters and may be collectivelyreferred to except where context indicates otherwise as “properties”,such as in an object oriented programming languages like Java or C++that characterizes light sources and lighting fixtures by many differentproperties that may be relevant to the use of light sources and lightingfixtures in a lighting installation. In these examples, the lightingobject properties may include physical dimensions (i.e., length, width,height, volume, and weight); lighting properties (i.e., output levels inlumens, Color Rendering Index (CRI) properties, color properties, colortemperature properties, spectral characterization properties, outputbloom properties, and many others); financial properties (e.g., prices,discounts, costs of operation, rebates, energy usage projections,installation costs, servicing costs, shipping costs, taxes, and others);performance properties (e.g., power levels, predicted life spans,battery life, operating temperatures); functional properties (e.g.,installation type, type and shape of connectors, etc.); controlproperties (such as for controlling light source outputs, networkingfeatures, IoT integration, remote control, autonomous control, etc.);and many others. In embodiments, light sources and correspondinglighting source objects 228 may include a wide variety of types of lightsources, as well as light sources in combination with optics, filters,and similar accessories. In these examples, the light sources may be,for example, incandescent, fluorescent, semiconductor, LED, halogen,laser, inductive and other light sources, and may be combined withfilters, phosphors, and the like. In further examples, a laser diodewith a single crystal phosphor may provide a highly variabledistribution from a light source that is projected into a room. Inembodiments, lighting may include laser-driven sources, such as using anarray of reflective elements to distribute light to a space. Inembodiments, the light sources and fixtures may be adjustable orcontrollable, and the lighting objects 226 may include properties,fields, metadata, and the like that enable dynamic adjustment or controlof the light source so that the lighting design environment 238 maymodel and display such control characteristics.

In embodiments, the lighting fixture objects 230 may include functionalelements of lighting fixtures, including without limitation roboticelements, such as where a lighting fixture is positioned on a gimbal orsimilar mechanism to allow directional control of the lighting fixture,where a lighting fixture is positioned for movement (such as along asupporting line), and the like.

In embodiments, the lighting objects 226, with their properties, may bestored in the lighting object library 232, which may include a universaldatabase of light sources and lighting fixtures from variousmanufacturers. In embodiments, the automated search system 234 of theplatform may search public sources, such as websites of lightingmanufacturers, sales representatives, and distributors, among manyothers, to find new lighting objects 226 for the library and to findupdated information (such as pricing and specification information)about the lighting objects 226 that are in the library. In theseexamples, the automated search system 234 may use a web crawler, spider,bot, or similar facility to find sources of information needed for thelighting object library 232. Where available, the automated searchsystem 234 may find and configure one or more application programminginterfaces to establish an ongoing feed of information to the lightingobject library 232, such as a feed of updated pricing or specificationinformation. The automated search system 234 may be configured to searchfor keywords relevant to lighting products (such as “lumens,”“illumination,” or the like, as well as terms used in engineeringstandards like ISO standards for lighting products), for file types thatare relevant (such as IES files that are used to characterize the farfield illumination characteristics of lighting products, as well asproprietary data formats used by various manufacturers to characterizetheir lighting products), for known brand names and product names, andthe like. In embodiments, the automated search system 234 may be trainedto find and classify lighting products, such as into categories, such asusing machine learning under human supervision. In embodiments, thecrowd-sourcing system 224 may be used to populate the lighting objectlibrary 232, such as by allowing users to identify and add additionallight sources and lighting fixtures, and by allowing users to populatevarious properties or fields of the lighting objects 226.

It will be appreciated in light of the disclosure that the marketplacefor lighting products is not transparent, as many products are promotedwith limited, often inaccurate technical information, and pricinginformation is often absent. Obtaining real pricing information,reflecting discounts, rebates, and the like often requires extendedresearch and negotiation. Similarly, availability and deliveryinformation is often unreliable. Accordingly, an operator of theplatform, by virtue of the handling of many projects on the platform,may accumulate, via repeated use of the automated search system 234, viaindependent testing of the lighting objects 226 and via feedbacksolicited from users and from the public, may accumulate, in thelighting object library 232, information about the validity of technicalspecifications, information that may augment technical specification,information about real product costs, and information about turnaroundtimes and the like. In embodiments, this information may be solicited bythe crowdsourcing interface 252 described elsewhere herein or via amanufacturer and the product feedback interface 236 through which usersor the public may provide information about their experiences withproducts and manufacturers. In embodiments, the resulting information(such as technical lighting information, pricing information, ratinginformation, and the like) may be stored in the lighting object library232 (such as in properties of each lighting object) or elsewhere foraccess by the platform.

In embodiments, the lighting design environment 238 may allow a user ofthe platform to select a lighting space model 214. FIG. 13 depicts auser interface 500 of the platform for a lighting space model selectionprocess of the lighting design environment 238. In embodiments, the userinterface 500 for a lighting space model selection process may includeone or more of a lighting space model selection initiation screen 502,lighting space model source selection screen 504, lighting space modelsource selection screen 506, lighting space model loading progressscreens 508 and a lighting space model selection output screen 510. Inembodiments, the lighting space model selection output screen 508 maypresent or render an output image of a scanned space.

FIGS. 14, 15, 16, and 17 depict lighting space model design renderingsprovided in a user interface 520 in accordance with many embodiments ofthe present disclosure. FIG. 14 depicts a design rendering 522 thatconveys an understanding of a space in terms of individual structuralelements. FIG. 15 depicts a final 3D design rendering of a space 524.FIGS. 16 and 17 depict 2D views 528, 530 of a design rendering.

FIGS. 18 and 19 depict scene renderings 540 in accordance withembodiments of the present disclosure. In the examples depicted in FIGS.18 and 19 , a user of the platform chooses a cleaning scene 542rendering to be displayed by the platform.

FIGS. 20 and 21 depict a user interface 550 for selection of lightingfixture objects 230 in accordance with the embodiments of the presentdisclosure. FIG. 20 depicts the UI 550 in which the platform tells auser the name 552 and specifications 554 of a lighting fixture object230, as well as presents options to modify 558 the selected lightingfixture object 230. FIG. 21 depicts the UI 550 that allows a user torearrange lighting fixture objects across a design.

FIGS. 22 and 23 depict various user interfaces 570 for AR functionalityfor user selection and modification of lighting fixture objects 230.Users of the platform 100, such as lighting designers, may desire toinspect and evaluate lighting fixture objects 230 by examining them andtheir light. To support this function, the platform 100 may allow a userto digitally inspect both the physicality and the light from a givenlighting fixture object 230.

In embodiments, the platform 100 may allow a user to digitally inspect alighting fixture object 230 using augmented reality (AR). Using AR, auser may inspect a lighting fixture object 230 with all its relevantdetails in the current lighting design environment 238 by viewing thelighting design environment 238 through a camera. By way of theseexamples, the platform 100 may modulate illumination intensity in aspace on a screen to show how a light from the lighting fixture object230 may affect the lighting design environment 238. This may allow auser to inspect both the physicality and the light of the lightingfixture object 230. FIG. 22 depicts the many examples of the userinterfaces for selecting and inspecting controlling the movement of alighting fixture object 230 in the augmented reality context depicted inFIG. 23 .

Near Field

FIGS. 24 and 25 depict various user interfaces 590 for near fieldfunctionality for the user-selection and modification of lightingfixture objects 230. In embodiments, the platform 100 may also allow auser to inspect a near field light characterization 200 of a lightingfixture object 230. Users, such as lighting designers, may desire toshine a lighting fixture object 230 on a white wall to be able toinspect it. In further examples, users, such as lighting designers, maydesire to shine a lighting fixture object 230 on any digital surface forexploration of one or more of other shapes, colors, textures, and thelike. The user may explore and perform these activities from variousdistances and angles. FIG. 24 depicts controls 592 of the platform 100that a user may use to move a lighting fixture object 230 and inspectthe light patterns the lighting fixture object 230 creates on a digitalwhite wall, or a digital surface, or the like. FIG. 25 depicts controls594 of the platform 100 that a user may use to change attributes of alighting fixture object 230 and inspect the light patterns the lightingfixture object 230 creates on a digital white wall, or a digitalsurface, or the like. These light patterns on the wall may change ifvarious parameters such as the beam angles, CCT and the like arechanged.

In embodiments, the platform 100 may support changing these and otherparameters and then render the light on the digital wall based on thechanged parameters. FIG. 25 depicts user interfaces 590 of the platform100 that a user may use to change such parameters. In embodiments, theinformation handled by the platform, such as information associated withor used by one or more lighting space models 214, the lighting spaceknowledge base 222, various lighting space objects 220, the lightingobject library 232, and other aspects of the platform, may be used tosupport the lighting design environment 238, which may include a userinterface supported by various information technology components,including processing systems, data storage facilities, operating systemelements, programs, applications, and the like. By way of theseexamples, the user interface may provide one or more visual displays ofa lighting installation, showing the lighting space containing variouslighting space objects 220 and the lighting objects 226. In embodiments,the visual representation generation engine 240 of the platform maygenerate views for the interface based on the various input datasources, object properties, and the like. Views from different inputsources 206 of the scanning system 102 may be presented in variousforms, such as in the native formats from the input sources 206 (e.g.,showing a point cloud from a laser scan, a photograph, a video, or aview generated from a CAD model, such as a 3D model), in normalizedformats (such as a 3D animation view or photo-realistic view that isgenerated by the visual representation generation engine 240), inaligned formats (such as showing overlays of different types of contentthat have been aligned, such as by the scan alignment system 210), sothat mixed content may be viewed in the interface including showing anobject from an infrared scan in the same space as a 3D modeled objectand a photographed item.

In embodiments, the visual representation generation system may beconfigured to allow user manipulation, such as by various tools, menuelements, mouse movements, touchscreen interactions, auditory inputs, orthe like. In these examples, a user may drag and drop various elementsinto the lighting design environment 238 (such as objects retrieved fromthe lighting object library 232 or the lighting space knowledge base222), position elements in the interface, size and resize elements,direct elements (such as directing a beam or bloom of light from alighting object in a desired direction), set properties of elements(such as setting desired colors from a color palette), and the like.Information about the properties of the lighting space objects 220 andthe lighting objects 226 may be used to inform how those objects arerepresented in the environment; for example, a lighting fixture thatgenerates a given beam shape may be automatically represented asdelivering that shape, and a light source with a given range ofproperties (such as intensity, hue, color saturation, CRI, dimming, orthe like) may be presented in the interface such that a user may adjustthe light source in the interface within the range of availableproperties. In embodiments, the user may, therefore, place a lightingfixture with a given light source into the environment, adjust itsposition and direction, and tune its intensity level, color, and thelike to a desired setting, or may configure it to be adjusted within arange of settings, such as setting it to dim along a given dimming curveas described elsewhere in this disclosure.

In embodiments, the lighting design environment 238 may include orenable a virtual reality (VR) interface 600, such as providing animmersive, 3D interface in which a user may experience the lightingspace, such as while wearing virtual reality headgear 602, or the like.In embodiments, the lighting design environment 238 may also include orenable an augmented reality (AR) interface, such as allowing a user toview an actual space (such as through a lens or camera) with overlayelements that represent the lighting space objects 220 and/or thelighting objects 226. By way of these examples, a user may look at aspace through glasses or headgear, and the lighting design environment238 may supply one or more overlays that represent how a lightingfixture might appear in the space (with the location being indexed toone or more features of the space by the visual representationgeneration engine 240) and how the beam or bloom of illumination fromthe fixture might illuminate other objects, etc. To support theinterface (including VR and AR interfaces), the visual representationgeneration engine 240 may be configured to handle various aspects of 3Dobject generation and manipulation, such as rendering hue, saturation,transparency/opacity, surface textures, shadows, specular/reflectiveeffects, and many others. FIGS. 26 and 27 depict VR interfaces 600 inaccordance with the embodiments of the present disclosure. FIG. 26depicts users of the platform sharing a virtual reality (VR) space 604to experience and modify a lighting design in a space. FIG. 27 depictsusers of the platform observing a design process 610 using a VRexperience 612.

As noted above, the platform for the design, fulfillment, deployment,and operation of a lighting installation, referred to as the platform100, may include augmented reality and virtual reality (AR/VR)functionality. In the examples, AR/VR functionality may demonstrate to auser of the platform how objects may be illuminated. In embodiments, theAR/VR functionality may show a user how a light distribution looks in aspace, by modeling the light distribution in a lighting space model 214,when a user desires to highlight a lighting object 226, such as apainting, within the space. In examples, a user may desire to mount oneor multiple lighting source objects 228 on a ceiling to highlight thepainting hanging on a wall. In embodiments, the AR/VR functionality ofthe platform may recognize objects or markers to build a virtual spacewithin the lighting space model 214, where the virtual space then getslayered on top of a camera feed to create an augmented reality world.

In these examples, the platform 100 may take a marker and position itflush with the base of a wall that is going to have the lighting object226 of interest placed on it. This provides the ground plane and thelocation of the wall that supports the lighting object 226, specificallythe painting in these examples while allowing a user to input theceiling plane information. Alternatively, the platform may determine theceiling plane information. Continuing with these examples, the user mayposition the one or more lighting source objects 228 where it makessense in the ceiling plane, for example avoiding ducts, or other objectsthat might interfere with the light. The light may project as a layeronto the surface that has the painting on it.

In embodiments, the user may see the light distribution as an alphachannel laid on top of a video feed. By way of these examples, theplatform may modulate illumination intensity in a space on a screen toshow how a light would affect the scene. In further examples, this maybe achieved using an alpha mask over the passthrough video. The platform100 may then allow the user to see how different products might look inreal time in the space.

In embodiments, the AR/VR functionality may also include handlingfaithful video color rendering. AR/VR functionality may handle faithfulvideo color rendering to develop an overlay by addressing nuances ofcolor perception and nuance of a video. AR/VR may do this by applying analpha map concept to render the video color as effectively as possible.An alpha map concept may include a color filter and an intensity map.

Lighting design may be only part of an experience when a user is in aspace. Toward that end, interior design may also contribute to theexperience of a user in a space. In embodiments, the user may enter aspace and desire to view the space when redecorated according to aspecific aesthetic requirement 116. The AR/VR functionality of aplatform may, therefore, allow a user to visualize a space redecoratedaccording to the aesthetic requirements 116. A user may interact withthe AR/VR functionality of the platform in this and other examples usinga phone 302, a phone accessory 304, such as an AR/VR headset, and thelike.

In embodiments, the AR/VR functionality may interact with themarketplace API 278 and fulfillment system 128 of the platform.Continuing with the previous examples, the AR/VR functionality mayinteract with the marketplace API 278 and fulfillment system 128 toorder the lighting source objects 228 required to meet the aestheticrequirements 116.

When a lighting designer has a design, while the designer may desire aclient to understand a design, the designer may not want to communicatethe design to a client using line drawings. Toward that end, AR/VRfunctionality of a platform may allow a designer and client to exist ina lighting space model 214 together virtually, such as with the Vive™ orthe Oculus Rift™ AR/VR devices, or the like. AR/VR functionality mayalso allow a user, such as a designer or a client, to move freely withina room-sized lighting space model 214. The hands of the user may betracked, allowing the user to do things within the room-sized lightingspace model 214.

In embodiments, the designer may be the master in a lighting space model214 and may have a high-end system that allows them to interact with thelighting space model 214 using the platform 100 connected to orintegrating with an AR/VR platform or device. For example, a client orclients may join the designer in the lighting space model 214 on anyAR/VR platform, for example, Google Cardboard™, as a platform maysupport any and all VR systems or just a video feed being displayed on aphone or laptop.

In additional examples, designers and clients in the same lighting spacemodel 214 may be able to talk to each other. As designers and clientswalk through the lighting space model 214, such as the lobby of thehotel, the clients may be able to say, “I don't like the way the signlooks and I want to change it.” In embodiments, the designer may bringup a virtual palette of lighting fixture objects 230, make the changessuggested by the clients, allowing the designer and clients toexperience everything about the space together in real time. In thisway, the designer may affect the lighting design, as well as showingvarious scenes, such as lunch, dinner, cocktail hour, etc. As such, theclient will have a complete understanding of the lighting design and askthe designer to make changes while having a complete understanding ofthe space and what is being offered. Toward that end, the designer andclient may obtain a richer understanding of how the lighting space model214 will look when implemented in a physical space.

In embodiments, the AR/VR functionality may include virtual showrooms.The virtual showrooms may operate as light-experiencing “caves” where auser may have their hands tracked. In these examples, the virtualshowrooms may allow a user to pick up a lighting fixture object 230,look at the near field characterization system 270 and see the effectsof the near field characterization system 270 on the lighting fixtureobject 230. The virtual showrooms may allow a user to control a lightingfixture object 230 in a multitude of environments, including being up atthe level of a cove and seeing how the scene may change as they adjustthe angle and position of the lighting fixture object 230.

With the Vive™ AR/VR device, for example, where a user has two trackedcontrollers, each with multiple inputs, a user may create a situationwhere a lighting fixture object 230 may be fixed to or “welded” onto theend of the controller in a virtual sense, so that the user may shine iton a wall and move it around a lighting design space model 214. By wayof this example, the user may control a light in a multitude ofenvironments. In addition, the user may position themselves up at thelevel of a cove and away from the ground to see how the scene changeswhen the angle and position of the light are adjusted in theenvironment.

In embodiments, the AR/VR functionality may include projecting lightfrom lighting source objects 228 in an AR environment onto an objectwhich exists on a physical surface in the physical (real) world. Forexample, a user of an AR device may be able to place furniture into alighting space model. In further examples, a user of AR/VR functionalitymay be able to place furniture into lighting design space model 214. Inthese examples, a user may drop one or more of the lighting spaceobjects 220 in a living room as the user looks through an AR device,while the AR/VR functionality scales the one or more lighting spaceobjects 220 for previewing the piece of furniture. In these examples,the lighting space object 220 may be a piece of paper being dropped on atable so the user may manipulate a lighting fixture object 230 in thelighting design space model 214 without having to physically have thelighting fixture object 230.

In embodiments, the lighting fixture object 230 acts like it is supposedto, including dropping a beam of light on the area around it. The usermay touch the lighting fixture object 230 to aim it. If the lightingfixture object 230 is aimed at the wall, then the user can, therefore,see beam angles, change beam angles and the like. This provides aneffective method for seeing how a lighting fixture object 230 would lookin the lighting design space model 214. In embodiments, the AR/VRfunctionality may include support for lighting fixture objects 230 thatmay articulate around two different axes. While the lighting fixtureobject 230 may be static, it may have multiple degrees of freedom. Inembodiments, the AR/VR functionality may be controlled by a joystickthat looks like an aerial view of a device that maintains itsorientation to a lighting fixture object 230. For example, if a userwalks around a lighting fixture object 230, the joystick may do a loopon screen, so the user is always dragging their thumb in a consistentdirection.

FIGS. 22 and 23 depict a user interacting with VR functionality tointeract with lighting fixture objects 230 that may articulate aroundtwo different axes. As depicted in FIG. 22 , a user may open an app byselecting the app from a plurality of apps, displayed on a mobile devicein this example. Continuing to refer to FIG. 22 , a splash screen mayload after the app is selected, as depicted in 572. Once the splashscreen is finished loading, the app may display different lightingsource objects 228 from which a user may select.

In these examples and as depicted in 574, an app displays “Rise” and“Troy” lighting fixture objects 230 to the user. As further depicted in326, the user may then select a lighting fixture object 230 from theoptions presented. In this example, the user selects the “Rise” option.The selected lighting fixture object 230 and the lighting fixture objectcontroller may then load, as depicted in 578. Continuing with theexample, the lighting fixture object controller allows the user to movethe selected lighting fixture object 230 in a 360-degree panningmovement as depicted in 580 and a 180-degree tilt movement as depictedin 582.

FIG. 23 depicts a user in the lighting design environment 238interacting with a lighting fixture object 230 according to the processdepicted in FIG. 22 . A lighting fixture object 230 pointing to theright of a user is depicted in 584. A lighting fixture object 230pointing to the left of a user is depicted in 586. A lighting fixtureobject 230 pointing toward the user is depicted in 588.

In another example, augmented-reality (AR) functionality or visualreality (VR) functionality may allow designers and clients in differentphysical locations to conduct a virtual walkthrough of a completeddesign, providing for an enhanced interactive experience between adesigner and clients. As illustrated by this example, AR/VRfunctionality may provide a shared space collaborative environment whereusers may view things on the fly, with a director controllingexperiences of viewers.

The lighting design environment 238 may be configured with the workflowguidance system 242 that may include workflow features that enablemanagement of projects (including multiple projects, such as formultiple lighting installations). The workflow guidance system 242 maysupport various workflow features, such as organizing a flow of workfrom initial concept work and ideation to finalizing a lightinginstallation, with many steps between. Workflow features may includechecklists, prompts, approvals, and the like that guide a user or groupof users through the end-to-end process, and various sub-steps, that aredescribed herein. The lighting design environment 238 may include acollaboration system 244, such as enabling various collaborationfeatures, including version control (such as allowing a designer tostore various candidate designs, and versions thereof) for a givenlighting installation 280 project, features for shared viewing ofdesigns, and other features that allow designers, owners and occupantsto collaborate on a design for a lighting installation. The term“occupant” or “owner” referring, except where context indicatesotherwise, to encompass customers and clients of designers, buildingowners, tenants, workers and other parties occupying the spaces in whichlighting systems are installed.

In embodiments, the library of templates 246 may be provided on theplatform with content that facilitates the development of a lightingdesign. Templates may be created for types of environments (offices,lobbies, guest rooms, bathrooms, etc.), for industries, for workflows,for overall aesthetic effects, and for many other purposes. Templatesmay be linked to requirements, such as various requirements describedbelow. In embodiments, the library of templates 246 may include ones forhotels, high-end residential lighting installations, restaurants,casinos, educational environments, healthcare environments, enterpriseworkplaces, manufacturing facilities, warehouses, and many others.

Whether starting with a template or not, the lighting design environment238 may be used to explore various design elements and variations. Theuser may discover, for example, by trying different lighting objects226, that a particular lighting design will work with a given lightingspace object (such as seeing that a given lighting design works wellwith a particular carpet or paint color depicted in the display of thedesign). For example, as the lighting design is changed in the lightingdesign environment 238, including controlling dimming or intensity of agiven lighting object, changes may be shown in the environment itself,such as how colors of paints and carpets appear, how shadows appear, andthe like. Thus, the user may experiment with various settings andconfigurations of the lighting objects 226 to explore the environment.In embodiments, the user may also change lighting space objects in thelighting design environment 238, such as changing paint colors, carpetcolors, and the like.

As depicted in FIG. 4 , the lighting design environment 238 may includea requirements system 106, where one or more requirements for thelighting installation 280 may be handled. Users, such as designers,owners, and occupants, may collaborate to develop requirements.Requirements may include financial requirements, which may be handled inthe budgeting system 248 of the platform, into which a user may enter atarget budget for a lighting installation. In embodiments, the budgetingsystem 248 may receive data (such via one or more APIs, by pulling datausing queries, by data migration techniques, by structured feeds, or thelike) such as from the lighting object library 232, such as pricinginformation for various light sources and fixtures that are of interestfor a lighting installation. In these examples, APIs may include a dataAPI, customization API, marketplace API and the like.

In embodiments, the requirements may also include technical lightingrequirements 108, such as relating to a number of fixtures, an amount ofillumination, a location of illumination, and many others. These may becaptured in the requirements system 106, depicted in FIG. 4 , for use inthe lighting design environment 238 and elsewhere in the platform. Inthese examples, lighting requirements 108 may relate to requiredintensity levels, contrast, CCT values, color temperature, colors, nearfield effects, far field effects, beam shapes, shadow patterns,reflected light characteristics, and many others. In embodiments, thelighting requirements 108 may be defined at the fixture level and alsoat the system level, such as based on the combined intensity generatedfrom multiple fixtures at a defined position and based on thedistribution and types of light sources and lighting fixtures within aspace. In embodiments, the technical lighting requirements 108 may beassociated with particular objectives, including functional andaesthetic requirements 116 described elsewhere in this disclosure. Inthe examples where lighting is for an office space, lighting may be madedirectional, such as to provide consistent illumination throughout aspace by having significant horizontal components in the direction oflight sources. Thus, lighting requirements 108 may include requirementsfor the overall composition of the lighting objects 226 and the lightingspace objects 220 in the space.

In embodiments, the requirements may include logistical requirements110, such as reflecting the planned timing of a project (includingvarious milestones for construction, renovation, move-in, and the like)for which the lighting installation 280 is relevant (such as indicatingthe required timing for installing a lighting system within the overallworkflow of a construction or renovation project).

In embodiments, the requirements may include the functional requirements112, such as reflecting functions to be supported or performed by alighting installation. The functional requirements 112 may includerequirements associated with defined work functions, processes, andworkflows, such as specifying high quality work lights for desks, tablesand other areas where work is performed, specifying appropriate levelsof general or ambient lighting suitable for a given type of environment(such as warm ambient lighting for a restaurant), specifying appropriatespot lights for highlighting elements (such as works of art), specifyinglighting systems for guidance of users in a space (such as to produce adesired traffic flow), and many others. In embodiments, the functionalrequirements 112 may be captured in the requirements system 106 for usein the lighting design environment 238 and elsewhere in the platform. Asdepicted in FIG. 3 , the functional requirements 112 may include controlrequirements 114, such as for controlling lighting fixtures (such as formotion, rotation and the like), for controlling light sources (such asfor dimming, changing colors, changing color temperatures, and thelike), and for controlling intelligent features of the lighting objects226 or lighting space objects, such as networking features (includinguse of routers, switches, gateways, bridges, mesh networking features,peer-to-peer networking features, optical networking features, Wi-Fi,Bluetooth, NFC and others), communication features (such as enablingremote control, such as from the cloud and enabling communication withother IoT devices), sensor features (such as using motion sensors,proximity sensors, and the like) and the like. In embodiments, each ofthese functional or control requirements 114 may correspond to one ormore properties of a lighting object that may be stored in the lightingobject library 232.

FIGS. 28 and 29 depict a user interface 620 of a requirements system106. In embodiments depicted in FIGS. 28 and 29 , a user of the platformmay set a budget requirement and/or various functional or aestheticrequirements for a design, using the budgeting system 248. While designand deployment of the lighting installation 280 involve many technical,functional, financial and logistical requirements 110, the lighting ofan environment is may also have aesthetic, emotional, and healthimpacts.

It will be appreciated in light of the disclosure that lighting may havea strong aesthetic impact. For example, painters and portraitphotographers develop distinctive lighting “set-ups” to createdistinctive aesthetic effects, as with the “Rembrandt set-up” that fullyilluminates one side of a subject's face and leaves a triangle ofillumination below the eye on the other side that is otherwise inshadow. Similarly, some restaurants may place spotlights in greetingareas and along pathways to tables to illuminate staff and patrons in afavorable way, while keeping ambient light levels low and warm toprovide a suitable atmosphere, while others might use brighterillumination and accent lighting to encourage a lively atmosphere. Itwill also be appreciated in light of the disclosure that lighting mayhave a significant emotional impact; for example, as incandescent lightsources are dimmed and become warmer, they are perceived by many asproviding a more romantic environment, while cooler light sources, suchas fluorescent lights, are perceived as reflecting a colder, moreclinical emotional state. Lights are often thought of as having “soft”or “hard” impacts on users. In embodiments, the various aesthetic andemotional impacts may be characterized by one or more aestheticrequirements 116, such term encompassing various aesthetic and emotionalfactors that may be intended for a lighting installation. In theseexamples, aesthetic requirements 116 may include a wide variety offactors and parameters. For example, where the lighting installation 280is intended to illuminate individuals, aesthetic requirements 116 mayindicate how individuals should be illuminated in various locationswithin the environment. In other examples, lighting for seats at ahigh-quality restaurant could be configured to use flattering lightingsetups, such as portrait lighting setups (e.g., split lighting setups,loop lighting setups, Rembrandt lighting setups, butterfly lightingsetups, broad lighting setups and short lighting setups, among others).

In embodiments, the lighting for a stage may be defined according tovarious stage lighting setups (including side lighting setups, frontlighting setups, colored lighting setups, flood lighting setups, beamlighting setups, spot lighting setups, and many others). Aestheticrequirements 116 may be captured in the requirements system 106 for usein the lighting design environment 238 and elsewhere in the platform.Such requirements may be specific (such as indicating use of a specifictype of spotlight of a given color temperature to achieve an aestheticeffect at a defined position in an environment); however, as with mostthings that have artistic or emotional impact, it may be difficult forusers to specify in detail what the lighting objects 226 will achieve adesired overall aesthetic or emotional effect. In embodiments,requirements may include compositional elements for rendering humans inexpected locations in a lighting environment. This may include variouselements that may make people look better, such as in the seats of arestaurant, or the like.

In embodiments, the compositional elements may be informed byinformation about the impact of lighting setups for various photographicstyles. As with lighting setups for photography, stage lighting, and thelike, the platform may define a set of aesthetic filters 118, each ofwhich defines a coordinated set of characteristics of a lightinginstallation, such as providing desired colors, color temperatures,contrast ranges, sharpness, illumination range, and the like. Similar tophotograph filters popularized by Instagram™, each aesthetic filter maybe used to specify or modulate one or more factors in a lightinginstallation, so that the lighting achieves a desired overall aestheticeffect. In embodiments, an aesthetic filter may include a data object ofa defined class, the class defining various properties or fields thatmay be varied to produce an aesthetic impact of the lightinginstallation 280 in an environment. In embodiments, aesthetic filters118 may be developed by users; for example, an owner of a brand (such asa hotel chain), may develop and define one or more aesthetic filters 118that define the aesthetic lighting properties for lobbies, guest rooms,conference rooms, and retail spaces for the chain. Similarly, an artist,designer, or the like may define lighting setups or aesthetic filters118 that are associated and branded by that designer, as with designerclothing, shoes and jewelry, such that a given lighting installation 280aesthetic may be obtained from that designer, such as via one or moretemplates that are controlled by the designer. FIG. 35 illustratesexamples of various filters 670 672, 674, 678 that may be used todetermine control parameters and other characteristics of a lightingdesign for an environment. FIG. 36 illustrates examples of alternativescenes 680, 682, 684, 688 that may be produced using aesthetic filtersin a lighting design.

In embodiments, aesthetic filters 118 may be crowd-sourced and/orcurated, such as by having users submit their own filters and/or byhaving users classify, rate, or comment upon filters. The owner of thebrand may further specify other elements, such as colors, objects, andthe like, which may be coordinated as an overall set of designrequirements for the brand. As with other requirements, aestheticrequirements 116 may be processed by the platform, such as toautomatically search for and find light sources or lighting fixturesthat satisfy the aesthetic requirements 116.

It will be appreciated in light of the disclosure that emotional andaesthetic effects may be quite complex, involving the interaction ofvarious factors to produce an overall effect. To facilitateunderstanding of emotional and aesthetic impacts of a lightinginstallation, an emotional content data structure 120 may be created,stored, and used in the platform, such as consisting of one or moreobjects or classes with various properties that may impact the emotionalimpact of a design. Relevant properties may include lighting properties,such as the distribution of light on the lighting space objects 220(such as the distribution of light on tables, desks, or workspaces),distribution of lights on walls, ceilings and floors, illuminationvalues, color and color temperature of light sources, spectral content(e.g., the quality and intensity of light at certain spectral ranges),and the like. In embodiments, the lighting fixture properties may alsobe included in the emotional content data structure 120, such asreflecting the impact of a given type of fixture (e.g., where thefixture has a given style or mood, such as “modern,” “retro,”“industrial,” “romantic,” or the like). Different types of fixtures mayhave different impacts, such as suspended fixtures versus embeddedfixtures. It will also be appreciated in light of the disclosure thatthe form factor of a fixture may also be important (and may reflecttrends in fashion that shift over time). Metadata for fixturescharacterizing general type, shape, and form factor may be included inthe lighting fixture object data structures and the emotional contentdata structure 120 used by the platform. In embodiments, variousfeatures such as stylistic and aesthetic features, may be extracted fromimages, 3D models, renderings, scans, or the like of existinginstallations or designs that are enabled or handled by the platform 100for use in characterizing an aesthetic filter 118 or for populating arelated emotional content data structure 120. Because a design style oraesthetic filter 118 may be a function of space, culture, geography andthe like, these factors and related parameters may be extracted and/ortaken into consideration in creating and/or characterizing an aestheticfilter 118 and/or emotional content data structure 120.

In embodiments, varying properties of the emotional content datastructure 120 may be used to develop or evaluate aesthetic filters 118,the lighting objects 226, lighting space objects, or other factors of adesign, such as using machine learning with feedback from users aboutwhat emotional or aesthetic impressions are created by variousconfigurations, so that various factors may be understood as tending toproduce a given set of emotional and aesthetic reactions. This mayinclude training a machine learning facility to algorithmically detectvarious characteristics that indicate an aesthetic or emotional effect.In these examples, a machine learning facility may be trained todiscover patterns of light and shadows that the lighting installation280 will create on faces, such as to identify known patterns (such as abutterfly shape around the nose in a butterfly lighting setup or atriangle of light under the eye in a Rembrandt lighting setup), so thatemotional impacts may be predicted.

In embodiments, machine learning may be used to improve aestheticfilters 118, lighting space models 214, or the like based on variousmeasures of feedback. These examples may include feedback based onreviewing displays of lighting designs in the lighting designenvironment 238 and feedback on lighting installations in the real world(including, without limitation, installations design and/or operatedusing the platform). Feedback measures may include feedback on socialimpact, mood, activity levels, revenues, returns on investment, beauty,usefulness, and many others.

FIGS. 30, 31, and 32 depict a platform user interface (UI) 630 forsetting filters and emotions of a design. FIG. 30 depicts a userselecting an experience emotional/aesthetic filter 632. FIG. 31 depictsa user adjusting a preset parameter 634 of a selected filter 638. FIG.32 depicts a user selecting fixtures 640 to which the selected filterwill be applied. In embodiments, the platform may include therecommendation engine 122, which may recommend light sources, lightingfixtures, or the like, using one or more machine-automated processes. Inthese examples, recommendations may be made based on history (such as bypresenting items that have been frequently specified in other lightinginstallations, items that have been reviewed most frequently insearches, and the like). Recommendations may be based on ratings, suchas from other users, such as collected in a rating interface 142 of theplatform. Recommendations may also be based on similarity, such as usingcollaborative filtering or similar approaches, such as based onsimilarity of an environment where the lighting installation 280 isplanned with other environments on various dimensions that are includedin lighting space models 214 and the lighting space objects 220 (such asby assigning weights to differences and similarities in two environmentsbased on the presence of similar objects in them). In embodiments,similarity for purposes of recommendations may also be determined basedon the requirements of users, such as by performing calculations onobjects in the requirements system 106. Similarity may also bedetermined based on the users, such as based on psychographic,demographic, preference and geographic factors.

In embodiments, the information about the preferences of a user may beaccumulated through interactions with a user, such as through the ratinginterface 142. By way of these examples, a user may be asked to rate acollection of lighting installations (such as being presentedphotographs or videos of installations in pairs, where the user is askedto select a preferred member of the pair, as in A/B testing). Inembodiments, a user may be asked to indicate preferences via a survey,such as with toggles or filters by which the user may indicate agreementor disagreement, or an extent to which one or more factors is importantto the user. In embodiments, lighting designs may be compared using theactive measurements of emotional reaction, such as by collecting inputfrom a physiological monitor, affect recognition system or other devicesfor measuring emotional reaction. In embodiments, the system may learnwhich types of installations the user prefers, either based on ahierarchy or classification of types of installations or by amachine-based learning, such as developing a self-organizing map. Theseand other techniques may be used to help sort and recommend one or moreitems to a user. In embodiments, the recommendation engine 122 mayrecommend one or more aesthetic filters 118 based on any of theforegoing factors.

In embodiments, the platform may include a user interface where a usermay scan or upload a digital representation or indicator of a space,such as one the user likes. By way of these examples, a user may scan amagazine photo that shows a space that is appealing, from which thesystem may automatically extract features, including the lighting spaceobjects 220, the lighting objects 226, and other features (includingaesthetic features), which may be used to inform the system of thecharacteristics or preferences of the user, such as to help identify andrecommend one or more aesthetic filters 118 to the user, or the like.

As noted, the design process may be iterative. In embodiments, the usersmay be prompted, such as in a workflow managed by the platform andreflected in the user interface, to enter information about budgets andother factors, such as the culture of a workplace, the preferred styleof a design, and the like. The system may automatically populate sets offixtures and light sources that generally satisfy various requirementswithin a given budget. In embodiments, manufacturers, such as owners oflighting brands, may sponsor one or more products, and the lightingdesign environment 238 may have locations for presenting one or moresponsored or non-sponsored products. In embodiments, a designer ormanufacturer may sponsor an aesthetic filter or lighting template. Inembodiments, a designer may mandate the use of particular fixtures orfixtures from a particular manufacturer. As a prototype design isdeveloped, the automated search engine may operate in the background ofa design project to suggest alternative light sources, fixtures, and thelighting space objects 220, such as ones that better fit requirements,that are available for a lower cost, that offer earlier delivery times,or the like. In embodiments, a user may be able to specify a lightsource of lighting fixtures that are not found by the automated searchengine, such as by defining various form factors and other properties,to create a request for a custom light source, which may be published,such as by a bidding interface 124 for bids by manufacturers. Forexample, a user may request an alternate color for a fixture, use of adifferent bulb as a light source in a fixture, a new control propertyfor a fixture, or the like.

It will be appreciated in light of the disclosure that lighting may alsohave a health impact. It is well known that light may impact circadianrhythms, affect mood, impact sleep, ameliorate seasonal affectiveeffects, impact concentration and performance (such as in educationaland work environments), feed photosynthesis, and have various otherphysiological effects on humans, animals, and plants. Thus, thefunctional requirements 112 may include health requirements 126 relatedto any of the foregoing, which may be captured in the requirementssystem 106 for use in the lighting design environment 238 and elsewherein the platform. By way of these examples, a designer of the lightinginstallation 280 for an educational environment may specify a givenquality of lighting for teaching spaces, such as one that improves focusand concentration or diminishes seasonal affective disorder. Similarly,a healthcare facility may specify lighting that improves mood during theday and improves the quality of sleep at night. As with otherrequirements, the health requirements 126 may be processed by theplatform, such as to automatically search for and find light sources orlighting fixtures that satisfy the health requirements 126 in accordancewith the embodiments of the present disclosure.

As noted above, various items may be solicited through a crowdsourcinginterface by which members of the public may be asked to provideinformation that improves one or more capabilities of the platform. Thismay include data on light sources and the lighting objects 226 (such aswhere manufacturers may populate such information to improve the qualityof information in the platform). In embodiments, the crowd sourcing mayalso include feedback and ratings, such as on lighting fixtures, lightsources, lighting designs (such as candidate designs and templateshandled by the platform), lighting installations (including onesdesigned with the platform and others), aesthetic filters 118, prices,and other factors. In embodiments, the crowd sourcing may provideverification, such as indicating whether a product is in fact what itclaims to be.

As noted above, designers, building owners, occupants, and otherrelevant parties may iterate, such as using collaboration features ofthe lighting design environment 238, to develop a lighting space model214 that reflects a series of passes at a lighting design and layout forthe lighting installation 280 in a space. In embodiments, an endcustomer may provide feedback, such as that the design is “tooromantic,” and the designer may modify a design, such as by selectingdifferent lighting objects 226 or applying a different aesthetic filterto modify the emotional or aesthetic impact of a design, such as bymodulating control parameters for the lighting objects 226 that areincluded in the lighting space model 214 or by substituting otherlighting objects 226. In the many examples, the designer, owner,occupant, or other involved parties may see each variant of a design ina realistic and immersive way, so that the overall aesthetic oremotional impact, and individual elements, may be experienced.

After iteration 104 (FIG. 1 ), a final design may be determined andcaptured in the lighting space model 214 for the project, with alighting project data structure 254 that represents all of the lightingspace objects and the lighting objects 226 in the model, along withtheir respective properties, as well as information about positions andorientations of the objects, functional and operational capabilities,and the like. In embodiments, the lighting project data structure 254may be used as an information source for the lighting schedule 256 forthe product, as well as for generating instructions, such as forinstallation, operation, and maintenance of the lighting installation.In embodiments, a lighting schedule system 258 may store a schedule,list, manifest, or the like (referred to herein as the “lightingschedule” 256) that contains information identifying the lightingfixtures and light sources that are included in a design (such as theones that are currently represented in a display in the lighting designenvironment 238 or that are currently represented in a version of alighting design for the lighting installation 280 project). Inembodiments, the lighting schedule 256 for a version of a lightingdesign may list the fixtures and light sources currently underconsideration (such as ones shown in a visual representation in thelighting design environment 238 that was created by the designer) alongwith other information, such as availability information, lead times,pricing information (including factoring in volume discounts, rebates,and the like) and other information that is relevant to budgetrequirements, lighting requirements 108, scheduling requirements andother requirements described throughout this disclosure. In embodiments,the automated search system 234 may search for light sources andlighting fixtures that may meet the requirements of the project, such asby searching the lighting object library 232 or public sources for thelighting objects 226 that could satisfy the requirements. Inembodiments, the lighting schedule system 258 may include automationfeatures, such as for filtering the lighting schedule 256 to includeonly items that satisfy one or more requirements, highlighting itemsthat violate one or more requirements, highlighting the extent to whichitems satisfy requirements to a better or worse degree (e.g., includingquality ratings for light sources or fixtures, or highlighting lowerprice versions of items of comparable quality), and the like. Inembodiments, the lighting schedule system 258 information mayautomatically feed into the budgeting system 248, such as by providing atotal cost or various sub-costs associated with a version of a design.In embodiments, a user may interact with the lighting design environment238 and the lighting schedule system 258 by adding or subtractingelements and viewing the impact on an overall budget and timing of aproject until a design is finalized. Upon finalizing a given design, thelighting schedule system 258 may generate the lighting schedule 256 forthe project, which may include all of the information necessary to orderthe light sources, light fixtures, and associated shipping andinstallation services, for the design. In embodiments, the lightingschedule 256 may be shared in the collaboration system 244, such as toallow approvals of particular light sources, fixtures, services, or thelike, and to allow approvals of budgets. The lighting schedule 256 mayalso be analyzed automatically by the requirements system 106, such asto confirm that the proposed lighting schedule 256 satisfies therequirements for the project. As noted above, the lighting schedule 256may evolve through one or more rounds of iteration 104 among designers,clients, and others, until a design for the lighting installation 280 iscomplete.

In embodiments, the lighting schedule 256 and/or the lighting projectdata structure 254 may be used in a fulfillment system 128 of theplatform, optionally along with other data sources, for automatedordering and fulfillment for a lighting installation. Using informationstored in the lighting schedule 256, a set of orders may beautomatically placed by an automated ordering system 130 of theplatform, including setting delivery dates and locations that correspondto the timeline for a project, such as dates of installation ofdifferent components of a projects (e.g., different delivery dates maybe configured for overhead lighting for the space that is integratedinto ceilings versus accent lights that may be installed later in aproject). In embodiments, a fulfillment system 128 and automatedordering system 130 may be included in the control IT infrastructure282. As depicted in FIG. 7 , the control IT infrastructure 282 may alsoinclude the lighting design environment 238. An order tracking system132 of the platform may track information, such as from shippingcompanies and carriers, such as to flag any issues in delivery that mayrequire changes to the schedule for a project. In embodiments, theautomated ordering system 130 may automatically order substitute itemsfor any items that are delayed, including on an expedited basis, or mayautomatically alert users to issues and offer alternatives, such asalternate items, expedited shipping, and the like. Thus, the fulfillmentsystem 128 may automatically undertake steps to keep a project on timeand on budget.

In embodiments, an installation guidance system 136 of the platform mayguide contractors, sub-contractors, installers, and the like to installthe lighting installation 280 according to the design that was createdin the lighting design environment 238. This may include providingstep-by-step guidance in locations, positions, control configurations,network connections, power connections, data interfaces, and otheraspects of each of the lighting objects 226 specified in the domain. Inembodiments, the installation guidance system 136 may access thelighting project data structure 254 to obtain information, such asinstallation instructions, configuration instructions, powerrequirements, and the like, for each of the specified lighting objects226 for a project. Guidance instructions may include visualrepresentations, video instructions, audio instructions, links tomanuals, and the like. In embodiments, the installation guidance system136 automatically configures an order of installation instructions forthe installer.

In embodiments, as the lighting objects 226 are installed in theenvironment of a lighting installation, networking featuresautomatically engage upon powering up one or more the lighting objects226, and the lighting objects 226 may automatically commissionthemselves, such as by connecting to the platform and/or to otherlighting objects 226. Thus, the lighting objects 226 in an installationmay self-commission and self-configure to create a network connectionbetween the lighting objects 226 in the environment and a remoteoperator (such as in the cloud). The lighting objects 226 may configurein a master/slave, ring, mesh, or peer-to-peer network, by whichautonomous control features may be engaged in the environment. Inembodiments, remote control features may be engaged using the networkconnection to the platform or other remote operators.

FIG. 37 illustrates exemplary embodiments of networked communication 700among components in a deployed lighting installation 702. Once installedand commissioned control of the lighting installation 280 may be handedover to an operator of a platform, such as a building owner, occupant,landlord, tenant, or the like. In embodiments, handoff may include usingidentity and authentication features, such as using keys, passwords, orthe like that allow operation of the lighting installation 280 bypermitted users. In embodiments, the remote-control interface 704 of theplatform may be used by an operator for remote operation of the lightinginstallation. The remote-control interface may use the lighting projectdata structure 254 as a source of knowledge about the properties,configurations, control capabilities, and other elements of a lightinginstallation, so that the same platform used for the design of thelighting installation 280 may be used to control the lightinginstallation. The remote-control interface may include operationalguidance features, such as guiding users through the operation of alighting installation.

In embodiments, the autonomous control system 262 may be provided for alighting installation, by which the lighting installation 280 maycontrol various features of the lighting system, such as based oninformation collected locally in the environment, such as from one ormore sensors. For example, the autonomous control system 262 mayautomatically adjust control parameters for a light source to achieveimproved adherence to the overall specifications for a lightinginstallation, may adjust timing variables based on detected usagepatterns in a space, may adjust lighting properties based on changes ina space (such as changes in colors paints, carpet and fabrics), and thelike.

Under operation, the lighting installation 280 may include theoperational feedback system 264, configured to collect information aboutthe lighting installation, which may include interfaces for solicitingand receiving user feedback (such as regarding satisfaction with theinstallation or indicating desired changes) and which may include thelighting installation sensor system 266, such as including lightsensors, motion sensors, temperature sensors, and others to collectinformation about the actual lighting conditions in the environment,activities of occupants within the environment, and the like.Information collected by the lighting installation sensor system 266 maybe relayed to a validation system 138 of the lighting platform, such asfor validation that an installation is operating as designed, includingby comparison of light properties at various locations in theenvironment with the specifications and requirements provided in thelighting design environment 238, such as reflected in the lightingproject data structure 254. In embodiments, the variances from thespecifications and requirements may be provided to the autonomouscontrol system 262 and/or the remote-control system, so that adjustmentsmay be made, either autonomously or by a local or remote operator of thelighting installation, to enable adjustments (such as to colors,intensities, color temperatures, beam directions, and other factors),such as to cause the lighting installation 280 to better meet thespecifications and requirements. The operational feedback system 264 mayalso capture feedback that leads to revisiting the lighting design inthe lighting design environment 238, which may induce further iteration104 as noted above, resulting in changes to control parameters for thelighting objects 226, as well as automated ordering of additional orsubstitute lighting objects 226, with updated installation andoperational guidance.

In embodiments, remote control may enable field programmable lightingsystems, such as for transitional environments like museums (where artobjects change regularly), stores (where merchandise shifts) and thelike as well as for customizable environments (such as personalizinglighting in a hotel room according to a specification 144 for a guest(which may include having the guest select an aesthetic filter). Suchfeatures may enable the lighting installation 280 to changeconfigurations (such as among different aesthetic filters 118) formulti-use environments, multi-tenant environments, and the like wherelighting conditions may need to change substantially over time.

In embodiments, a lighting system may include navigation features, suchas being associated with beacons, where the lighting system interactswith one or more devices to track users within a space. The lightingobjects 226 and their locations may be associated with a map, such asthe map of the lighting space in the design environment. The map may beprovided from the lighting design environment 238 to one or more otherlocation or navigation systems, such that locations of lights may beused as known locations or points of interest within a space.

In embodiments, the lighting installation 280 may be designed for anoperation that is coordinated with one or more external systems, whichmay serve as inputs to the lighting installation, such as music, videoand other entertainment content (such as to coordinate lighting withsound). Inputs may include voice control inputs 708, which may includesystems for assessing tone or mood from vocal patterns, such as toadjust lighting based on the same.

In embodiments, inputs may also include inputs from wearable devices,such as enabling adjustment of lighting control parameters (autonomouslyor with remote or local control features) based on physiologicalfactors, such as ones indicating health conditions, emotional states,moods, or the like. Inputs from wearable devices may be used in theoperational feedback system 264, such as to measure reactions tolighting conditions (such as to enable automated adjustment of alighting installation), as well as to measure impacts on mood, healthconditions, energy, wellness factors, and the like.

In embodiments, the platform 100 may be configured to change settings orparameters for a lighting installation (including various lightingobjects and fixtures, such as using a custom tuning system) based on avariety of real time data, with a view to having the lightinginstallation best suit its environment in a dynamic way. In embodiments,data may be obtained that serves as an indicator of the emotional stateor the stress level of an environment, and the lighting installation mayrespond accordingly to that state or stress level. In embodiments, dataabout the environment may be collected by a wearable device, such as asmartwatch, armband, or the like; for example, data may be collected onacceleration, location, ambient light characteristics, and heart rate,among other possibilities. In embodiments, the data may be provided tothe platform 100 for analysis, including using machine learning, such asto observe physiological indicators of stress, mood, or the like undergiven lighting conditions. The analysis may enable model-based controls(such as where a given mood or state of the users in a room are linkedto a set of control parameters appropriate for that state). Inembodiments, machine learning may be used; for example, over time, byvariation of parameters for lighting objects and fixtures (such ascolor, color temperature, illumination patterns, lighting distributions,and many others), a machine learning system may, using feedback onoutcomes based at least in part on physiological data and other datacollected by a wearable device, select and/or promotion lightinginstallation parameters that improve various measures of stress, mood,satisfaction, or the like. This may occur in real time under control ofa machine learning system based on the current conditions of users orthe environment. In embodiments, data collected at least in part by aphysiological monitor or wearable device may be used as an input toprocessing logic on a lighting object that changes lighting levels orother parameters to accommodate the ‘emotional state’ of the users in anenvironment where the lighting object is located. In embodiments, thereis memory that retains and manages function with no appreciable drain onthe battery.

In embodiments, inputs may include systems that take data harvested fromsensors 710 in the lighting installation environment as well as sensorsthat reflect information about users, such as one or more ofphysiological sensors (including wearable devices, such as armbands,wrist bands, chest bands, glasses, clothing, and the like), sensors onvarious devices used by a user, ambient sensors, and the like. These mayinclude sensing one or more of temperature, pressure, ambient lightingconditions, localized lighting conditions, lighting spectrumcharacteristics, humidity, UV light, sound, particles, pollutants, gases(e.g., oxygen, carbon dioxide, carbon monoxide and radon), radiation,location of objects or items, motion (e.g., speed, direction and/oracceleration). Where one or more wearable or physiological sensors areused, they may sense one or more of a person's temperature, bloodpressure, heart rate, oxygen saturation, activity type, activity level,galvanic skin response, respiratory rate, cholesterol level (includingHDL, LDL and triglyceride), hormone or adrenal levels (e.g., cortisol,thyroid, adrenaline, melatonin, and others), histamine levels, immunesystem characteristics, blood alcohol levels, drug content, macro andmicro nutrients, mood, emotional state, alertness, sleepiness, and thelike.

In embodiments, the platform may connect to or integrate with datasources of information about users, such as including social networks(Facebook™, LinkedIn™, Twitter™, and the like, sources of medicalrecords (23&Me™ and the like), productivity, collaboration and/orcalendaring software (Google™, Outlook™, scheduling apps and the like),information about web browsing and/or shopping activity, activity onmedia streaming services (Netflix™, Spotify™, YouTube™, Pandora™ and thelike), health record information and other sources of insight about thepreferences or characteristics of users of the space of a lightinginstallation, including psychographic, demographic and othercharacteristics.

In embodiments, the platform may use information from sources thatindicate patterns, such as patterns involving periods of time (dailypatterns, weekly patterns, seasonal patterns, and the like), patternsinvolving cultural factors or norms (such as indicating usage patternsor preferences in different regions), patterns relating to personalityand preferences, patterns relating to social groups (such as family andwork group patterns), and the like. In embodiments, the platform maymake use of the data harvested from various sources noted above to makerecommendations and/or to optimize (such as automatically, undercomputer control) the design, ordering, fulfillment, deployment and/oroperation of a lighting installation, such as based on understanding orprediction of user behavior. This may include recommendation oroptimization relating to achieving optimal sleep time and duration,setting optimal mealtimes, satisfying natural light exposurerequirements during the day, and maintaining tolerable artificial lightexposure levels (such as during night time). In embodiments, theplatform may anticipate user needs and optimize the lightinginstallation to enhance productivity, alertness, emotional well-being,satisfaction, safety and/or sleep.

In embodiments, the platform may store a space utilization datastructure that indicates, over time, how people use the space of thelighting installation, such as indicating what hallways are moretrafficked, and the like. This may inform understanding of a space, suchas indicating what is an entry, what is a passage, what is a workspace,and the like, which may be used to suggest changes or updates to alighting design. In embodiments, sensors may be used to collect and readwhere people have been in the space, such as using one or more videocameras, IR sensors, microwave sensors. LIDAR, ultrasound or the like.In embodiments, the platform may collect and read what adjustmentspeople have made, such as task lamp activation and other activities thatindicate how a lighting fixture is used by an individual in a space. Byway of these examples, aggregate usage information may be used tooptimize a lighting design and adjust other lighting designs. Based onthese factors, a space may be dynamically adjusted, and the lightingmodel for an installation may be updated to reflect the actualinstallation.

In embodiments, control capabilities of the lighting objects 226 mayinclude dynamic configuration of control parameters, such as providing adimming curve for a light source that is customized to the preferencesof a designer or other user. This may include a selection from one ormore modes, such as ones that have desired effects on mood or aestheticfactors, that have desired health effects, that meet the functionalrequirements 112, or the like. By way of these examples, when using afour-channel color changing system with both uplight and downlightcomponents, the platform may include multiple modes, such as two modesthat may help mimic outdoor lighting. In these examples, a first modemay include a downlight portion of the light that may be set in acircadian mode, such as being set at warm or very warm (1800-2500K CCT)around sunrise and sunset and transitioning to cool to very cool(5000-10000K CCT) around noon. In embodiments, these could be white orslightly hue-adjusted white color points. Additionally, an up-lightportion may be made to mimic sky color. When a downlight portion islighting a room with a warm CCT like 2500K, an up-light portion may beused in further examples to broadly illuminate the ceiling, such as in ashade of orange to mimic the sunrise or sunset. During the middle of theday, while the down-lighting is a cool white CCT, an up-light may infurther examples illuminate the ceiling in a cyan shade to mimic amid-day sky.

In order to truly achieve circadian action, prolonged exposure may berequired, however, a melanopic flux may, in many embodiments, need to beat least 10:1 and in further embodiments, may need to be 20:1, 50:1,100:1, or a greater ratio. It will be appreciated in light of thedisclosure that most conventional systems simply adjust from a warm CCTto a cool CCT, which may only provide a 2:1 or 3:1 ratio of melanopicflux, which may not be enough to provide health benefits. Inembodiments, the platform may include spectral tuning targets for amultiple channel system (e.g., a four-channel system) that may optimizethis ratio for a white only ceiling and/or a white plus sky colorceiling, among examples. These targets, along with adjustments intensityof light (e.g., 4:1) may provide a higher ratio, such as a 10:1 ratio orgreater, and thus provide greater melanopic flux ratios.

In a second mode and either in combination with the above mode or not,the platform may include adjustable down-facing optical distributions tosupport an ability to shift the bias of light in a room. In embodiments,the user may synchronize the general illumination (middle of the room)lights to start off with most of the light bias “left” (looking at alinear fixture end-on), shift to being primarily pushing down themiddle, and then biasing the light to the “right” over the course of theday. These modes may support an ability to have the diffuse shadows castin a room mimic the movement of the sun across the sky in examples wherethe room may not align with the East-West movement of the sun but themodes may nevertheless support generating a lighting bias typical ofbeing outside.

In embodiments, various other programmable modes may be provided, suchas fixture settings where using different combinations of color lightsources to achieve a given mixed color output may be optimized forefficacy, efficiency, color quality, health impact (e.g., circadianaction), or to satisfy other requirements. In embodiments, theprogrammable modes may also include programmable dimming curves, colortuning curves, and the like (such as allowing various controlinterfaces, such as extra-low voltage (ELV) controllers or voltage-baseddimmers to affect fixture colors, such as where a custom tuning curveprovides a start point, an end point and a dimming and/or color tuningpath in response to a level of dimming). In embodiments, programmablemodes may use conventional tuning mechanisms, such as simpleinterpolation systems (which typically use two or three white colorLEDs, are dimmable on a zero to ten-volt analog system, and have asecond voltage-based input for adjusting the CCT of a fixture betweenwarm and cool CCTs. FIG. 38 illustrates a system 750 using three whitesources with CCTs at 2700K, 4000K and 6500K. In embodiments, variousspectral tuning curves 760, 762, 764, 768 may be provided, asillustrated in FIGS. 39A, 39B, 39C and 39D. In embodiments, FIG. 39Adepicts a black body curve 770. By way of this example, FIG. 39B depictsa curve 772 having a constant CCT at 6,500K. FIG. 39C depicts a curve774 having a color space perimeter. FIG. 39D depicts a curve 778 havinga random color tour. In embodiments, programmable modes may also beprovided for fully tunable systems, which may include various lightsources, such as various combinations of red, green, blue, white andamber LEDs, such as RGB, RGBW, RGBWA (red, green, blue, white andamber), and other three-channel or greater color combinations. Becauseof the wide range of potential white or non-white colors produced bysuch systems, they may be controlled by the platform 100 that mayspecify a particular x, y coordinate on the CIE diagram 780, asillustrated in FIG. 40 . Lighting control protocols like DMX™ and Dali2.0™ may achieve this result. The black lines 782 show the available CIEarea covered by an RGB system, with the white lines representing thecolor contributions from each channel.

In embodiments, a programmable color curve 790 for an LED driver may beinput, such as through an interface 792 of the platform 100, or througha desktop software interface, a mobile phone, a tablet app, or the like,that enables a user to define a start and stop point to a color tuningcurve and to specify how it will be controlled by a secondary input,such as a voltage-based input (e.g., a 0 to 10-volt input) to thefixture. These may include pre-defined curves, as well as the ability toset start, end, and waypoints to define custom curves. Referring to FIG.41 , the color curve 794 shows a starting point around 8000K biasedabove the black body curve, the color curve crossing the black bodyaround 2700K, and finishing around 1800K below the black body curve.Similarly, a curve could be programmed such that the start was 4000Kwell above the black body, with the end being 4000K well below the blackbody. By way of these examples, any adjustment would be in hue only, notCCT. Further examples may include a curve that never produces a whitecolor, such as starting in the purple and finishing in orange. In any ofthese cases, these curves may be programmed into lighting fixtures viathe interface of the platform 100, the desktop, mobile phone or tablet.In embodiments, the curves may be designed, saved, and then activated,such as using the secondary (supplemental) 0 to 10-volt input.

In embodiments, a three-channel warm dim mode may be used, such as fortarget applications where the “fully on” CCT falls between 3000K and2500K. By way of these examples, as the fixture dims (via ELV control orin response to the 0 to 10-volt input) the CCT may be graduallydecreased to between 2500K and 1800K. In embodiments, the hue adjustmentmay all occur below the black body curve. Alternative embodiments mayuse the “Green” and “Red” points, such as of a four-channel system, plus4000K to achieve a warm dimming mode that allows for adjustment bothabove and below the black body curve.

Where a four-channel color system includes 3000K to 1800K CCT whitewithin its range, a programmable mode may be included within the driverthat adjusts color with the dimming percentage as well. In some aspects,this may be similar to a conventional control mode, except that thecolor control would not be on the secondary 0 to 10-volt channel, butmay be activated through the primary 0 to 10-volt input channel or ELVcontroller. In embodiments, the “starting” color point may be the onewhen the fixture was “fully on.” In embodiments, the “ending” colorpoint may be the one where the fixture is maximally dimmed. It is thuspossible to make full range color change, such as purple to orange, thatis slaved to the 0 to 10-volt or ELV dimming signal.

In embodiments, an optimized mode may be provided. With a 4-channelcolor system, there are many ways to create a single x-y point on theCIE diagram. In embodiments, the maximally efficient mode may typicallybe one that uses the colors that have x, y coordinates closest to thetarget x, y coordinate. But for best color quality, utilizing a fourthchannel (and thereby requiring more light from the color in the opposite“corner”) may help provide a desired spectral power distribution. Forthe maximum melatonin suppression (for systems hoping to mimic circadianlighting), a higher cyan channel content may be required for CCTs of3500K and above and minimizing cyan and blue content below 3500K. Itwill be appreciated in light of the disclosure that conventional systemseither require expert users to understand the color balances necessaryto achieve these effects (who then implement the color balanceschannel-by-channel) or are designed for maximum efficiency with colorquality as a byproduct.

In embodiments, a digital power system is provided herein (includingfirmware-driven power conversion and LED current control) that controlsa multichannel color system, such as a 4-channel color system, andallows for the inclusion of “modes” which may calculate the correctcolor balance between the various channels to provide optimized outputs.In embodiments, optimization may occur around one or more of efficacy,color quality, circadian effects, and other factors. Other modes arepossible, such as optimizing for contrast, particular displayrequirements (such as red-emphasized colors for displaying meats orgreen-emphasized colors for displaying produce, among many others). Itwill be appreciated in light of the disclosure that this is not anexhaustive list but is representative of potential modes that could beengaged through an interface of the platform (or of a mobile, tablet ordesktop application) where a color tuning curve may be specified, suchthat the curve is used to specify an interface between a controller andthe Digital PSU in a lighting fixture. In embodiments, these modes mayaccount for actual measured colors for each fixture and calculate thecorrect balance of for the chosen modes, such as based on algorithmsloaded into the Digital PSU microprocessor.

In embodiments, machine learning may be used, such as based on variousfeedback measures, such as relating to mood (stated by the user ormeasured by one or more sensors), noise levels (such as indicatingsuccessful utilization of a space based on a desired level of noise),returns on investment (such as where lighting systems are intended topromote retail merchandise), reported pain levels, measured healthlevels, performance levels of users (including fitness, wellness, andeducational performance, among others), sleep levels, vitamin D levels,melatonin levels, and many others. In embodiments, the lightinginstallations may be operated or controlled based on externalinformation, such as based on seasonal lighting conditions, weather,climate, collective mood indicators (such as based on stock market data,news feeds, or sentiment indices), analyses of social network data, andthe like. This may include controlling a system to reflect, orinfluence, the mood of occupants.

It will be appreciated in light of the disclosure that most lightingproducts are characterized by IES files that provide far field dataabout light output; however, the near field characterization system 270is usually absent or inaccurate. Moreover, lighting fixture objects 230may be modeled with Illuminating Engineering Society (IES) data but IESdata may lack near field fidelity making a lighting simulationincomplete and in some cases, inaccurate. In embodiments, the platform100 may include a solution, a near field characterization and/or testingsystem, to capture near field light distribution, so that the platform100 may more completely and accurately model lighting.

It will also be appreciated in light of the disclosure that lightingfixture object manufacturers may provide IES files for their lightingfixtures. IES files may typically consist of far field lightdistribution data. IES files may include a two-dimensionalcharacterization I (θ, φ), which may represent the luminous flux in agiven direction defined by θ and φ. In embodiments, this data may begathered using a goniophotometer to measure light at different anglesand at large distances relative to the size of a lighting fixture suchas more than 10× the size of a lighting fixture object 230. Bydefinition, the far field characterization may assume that a lightingfixture object 230 is a point source, which may be a valid assumption aslong as the lighting fixture object 230 is at a large enough distancefrom the measurement system. It will be appreciated in light of thedisclosure that in practice many lighting fixture objects 230 are areasources with highly varying light distributions in different directions.With this in mind, their characterization as a point source mayfrequently fail to capture the lighting field patterns that may beobserved at shorter distances from the fixture, which are referred toherein as the near field. Near field light distribution is defined asthe luminous flux in a given direction per unit area of a source. Inembodiments, near field light distribution may be characterized by fourdimensions in a function L (x, y, θ, φ) which accounts for the spatialextent of the source. Because this is a significantly richercharacterization, the tools in the many embodiments may be used tocapture the lighting field patterns observed in the near field as wellas in the far field. Toward that end, far field characterization may becalculated by integrating over x, y. In embodiments existing near fieldmeasurement systems may be used, employing the same concept as agoniophotometer. The approach may include rotating a lighting fixtureobject 230 to measure the luminance of a lighting fixture object 230 atall angles. For example, the PM-NFMS™ from Radiant Vision Systems™consists of a two-axis goniophotometer to rotate a lighting fixtureobject 230 and a stationary imaging colorimeter that is placed in thenear field to view the lighting fixture object 230 directly. Inembodiments, the high resolution of the CCD sensors in the imagingcolorimeter enables distinguishing of individual rays and the system maythen produce a near field model of luminance. It will be appreciated inlight of the disclosure that this method, although accurate, has thefollowing disadvantages: (a) slow speed as it may take anywhere between2-5 hours to measure near field light distribution for a lightingfixture; (b) high cost as the system may cost tens of thousands ofdollars or more; (c) limited portability; and (d) long setup time. Inembodiments, the platform 100 may, in contrast, use an indirectmeasurement system. An indirect measurement system may include indirectmeasurement hardware and indirect measurement software.

FIG. 9 depicts a near field characterization flow 300, which may beintegrated with or may provide information to the platform 100 inaccordance with the embodiments of the present disclosure. In a nearfield characterization flow 300, a light field 310 may be measured, suchas using inputs from a camera-enabled phone 302, a sensor-based system306, a handheld scanner 308, or the like. In embodiments, a phone mayinclude the phone accessory 304. Measurement of the light field 310 maybe provided as an output to other elements of the platform 100, such asto a volumetric render 312, a source model calculation 314, or the like.In embodiments, the source model calculation 314 may then be provided toa renderer 316 and/or a far field system 318.

As depicted in FIG. 10 , an indirect measurement system 400 may includeexamples of a sensor-based system 306. Using the indirect measurementsystem 400 instead of measuring the luminance of a lighting fixtureobject 230 directly, a lighting fixture object 230 may be set-up inindirect measurement hardware to illuminate an intermediary orintermediate surface. In embodiments, the intermediary or intermediatesurface may be an imaging screen 402 that may be translucent. Inembodiments, the lighting fixture object 230 may light up one side ofthe screen 402 and may create an illuminance pattern 404 which may bevisible on the other side of the screen 402. This illuminance pattern404 may then be captured via a camera 408. In embodiments, the lightingfixture object 230 may sit on a positioning slide 410 to enable it tomove, in order to vary the distance between the lighting fixture object230 and the screen 402.

As depicted in FIG. 10 , the platform 100 may begin by placing alighting fixture object 230 as close to the screen 402 as possible andthen move it in small increments through a certain distance, whilecapturing the illuminance pattern 404 at every distance increment.Because each measurement is a 2D image, the set of measurements may giverise to 3D volumetric illuminance data. Once the indirect measurementhardware captures 3D volumetric illuminance data, the indirectmeasurement software may use reconstruction techniques to create a nearfield characterization of the source. The indirect measurement softwaremay model a lighting fixture object 230, as an area source that includesa certain number of point sources depending on the desired accuracy, thehigher the number of point sources, greater the accuracy of the system.

Each point source may be characterized by I (θ, φ), namely, the luminousflux it gives out in every direction. That characterization combinedwith the (x, y) position of each point source on the fixture surface,may provide the near field characterization L (x, y, θ, Φ). Inembodiments, the relative contribution of each point source to eachpixel in an illuminance pattern may be a function of: (i) the distancebetween a physical location of a point source on a lighting fixtureobject 230 and a physical location of a pixel on a screen; (ii) theangle a line connecting the above two points makes with a normal to ascreen; (iii) optical properties of a screen; and (iv) luminance of apoint source in a direction of a pixel. Because the items (i), (ii), and(iii) in this list may be known quantities, this problem may beformulated as a system of linear equations, which may relate unknownluminance and measured illuminance. In these examples, Ax=b, where “A”is a (m×n) matrix determined by the above three factors: (i), (ii), and(iii), “x,” a (n×1) vector is a collection of variables—each unknownvalue of L (x, y, θ, φ), and “b”, a (m×1) vector is a collection of eachpixel value from the 3D volumetric illuminance data.

In the above system example, “A” is known, “b” is measured and hence “x”may be calculated. In embodiments, the platform 100 may use numericalmethods to solve linear systems involving a large number of variables.For example, an iterative algorithm may be used to solve a system oflinear equations, the Kaczmarz method being one possible example. Thisindirect measurement system 400 may be much faster than existing systemsand may be less expensive than current systems of the art. Inembodiments, the indirect measurement system 400 may use smartphonecameras. Use of smartphone cameras may make the indirect measurementsystem 400 very portable and democratize access to near field lightdistribution data. Near field light distribution may be a high-fidelitymodel of the one or more lighting source objects 228 (FIG. 8 ), whichmay enable much more accurate renderings of spaces using the lightingsource objects 228. In embodiments, this may have applications fromarchitecture to Hollywood. Additionally, a near field may be used todefine certain lighting fixture object 230 evaluation metrics.

Referring to FIG. 11 , based on a near field light distribution 430, newnear field metrics may be developed that may be used to evaluatelighting fixture objects 230. In embodiments, these metrics may dependon the distance and angle to a lighting fixture object 230, so having arich characterization through the near field light distribution 430 maybe useful for many situations. Exemplary metrics may include: (a) ascale of artifacts metric 432 such as indicating the size and scale ofartifacts and/or the frequency with which they appear in the lightpattern; (b) a mixing distance metric 434 such as indicating thedistance from a lighting fixture object 230 at which the light mixeswell and has zero or minimal artifacts; and (c) a contrast in near fieldmetric 438 (e.g., indicating the min-to-max intensity ratio in the nearfield that shows how dramatic the lighting patterns/artifacts are). Inembodiments, the light source test system 272 (FIG. 8 ) of the platformmay be used to test the lighting objects 226 and augment theirproperties with complete near field and far field data, so thatillumination effects may be accurately characterized in the lightingdesign environment 238 for objects at varying distances from a lightsource.

Near Field: Observing a Light Field and not Point Sources on the LightSource

In embodiments, illumination data for or about a light source mayinclude near field data, far field, and a combination thereof,reflecting the nature of illumination of the environment surrounding thelight source at different distances. Near field data may be capturedproximal to a light source and may generally describe illuminationeffects that tend to be non-uniform in distribution as a result of theproximity to the source. For example, a lighting fixture with multiplelight sources may have a near field illumination pattern that representsthe super-position of the light from each bulb, which may varysignificantly at different angles from the light source. Meanwhile, formost light sources, such as fixtures with multiple bulbs, far field datamay represent the distribution at greater distances, where illuminationpatterns become more uniform, such that a distribution approximates thelight source as if it were a single, point-based light. Far field datais often used to measure and characterize light sources, but such farfield data is usually missing potentially valuable information about alight fixture, including how the illumination from the light source islikely to interact with surfaces, objects and the like in the nearbyenvironment. Relevant information, such as how each bulb in a fixturemay contribute to a lighting effect, can be lost in far field data, asthe contributions from multiple bulbs tend to merge at a distance fromthe light source. At any given location in the near field, illuminationdata may represent illumination effects from one or more light sourcesin the fixture, as well as how other elements of a fixture, such aslenses and other optical elements, filters, shades, reflective surfaces,and the like may impact certain aspects of directionally oriented lightfrom one or more bulbs. Therefore, methods and systems are providedherein for capturing illuminance patterns that may be present at variouslocations and distances proximal to the light fixture, including toallow characterization of the light fixture, such as for use in alighting design system, a lighting marketplace or other system orprocess described throughout this disclosure.

In embodiments, methods and systems for capturing and handling nearfield illumination information may include one or more methods ofgenerating a near field illumination data structure. Such a datastructure may characterize near field patterns and the like generated bya light source, including, in embodiments, a multi-bulb source.Gathering the data for the data structure may be accomplished bydisposing a surface in the near field of the light fixture so that atleast one side of the disposed surface is illuminated by the lightsource. The surface may be positioned at a range of locations, such asdifferent orientations relative to and distances from the light source,so that illumination data for a range of portions of the near field maybe captured, optionally including or representing different slices ofthe surface through the near field illumination pattern around the lightfixture. In embodiments, one or more two-dimensional image sensors maybe disposed relative to the surface, such as in front of the surface,behind the surface, adjacent to the surface, above the surface, belowthe surface, and the like, to capture light in the near field. Invarious embodiments, light reflected from the surface may be captured,light that passes through the surface may be captured (such as if thesurface is at least semi-translucent), light that illuminates thetwo-dimensional sensor directly may be captured, or combinations of theabove may be used. In embodiments, light captured by a two-dimensionalimage capture array may be converted by the array into digital valuesrepresenting at least a measure of light brightness for each cell in thearray. This illumination data may be populated into the data structure,such as in a planar image. As the location and distance of the surfaceand/or sensor is changed, illumination data for a plurality of distancesand positions (including angular orientations) in the near field can bepopulated into the data structure. In this way, the data structure cancapture the light intensity, color, color temperature value (e.g., CCT)and the like for each cell along with a distance from the light source,an orientation of the sensor, an orientation of the surface associatedwith the sensor, and the like.

In embodiments, the orientation of the sensor may remain fixed while thedistance from the light source is varied. This may be accomplished witha variable distance device that facilitates varying a distance betweenthe light source and the sensor and/or surface. Data captured in thisway can represent a directional catalog of illumination data,effectively producing a plurality of incremental-distance differentiatedimages of luminance of the light source. When distance variances arecombined with position, such as latitudinal or longitudinal position,angular position or the like, data in the data structure can accuratelyrepresent the near field illumination as it would occur at particularlocations in an environment, such as where light would interact with asurface that is positioned at a given distance and orientation relativeto the light source. When latitudinal and longitudinal positionvariations are both included, an omnidirectional volume of illuminationdata may be captured.

In embodiments, storing the near field images for a plurality of lightsources in a digital library, such as a database, may facilitate a usersearching the library to identify a light source having a desiredpattern of illumination, at least for a near field. The digital librarymay include or interface with a pattern matching system for matching atleast one image in the library to a specified or desired pattern ofillumination for a space. Such a pattern matching system may facilitateidentifying at least one light source (e.g., fixture) that can providethe specified pattern. In embodiments, a user may, such as byinteracting with a software-based user interface, draw an image of apattern (such as outlining a shape, selecting a color and the like) orselect an image of a desired pattern of light (such as a “bloom” oflight on a wall), and the pattern matching system may search formatching images in the digital library. In embodiments, pattern matchingmay use machine learning, such as by training the system to findmatching patterns based on a training data set created by human usersand/or supervision of a series of trials by a machine learning system,such as a neural network. Machine learning may operate on featurevectors of the images of an illumination pattern, such as intensities,colors, shapes and the like. The pattern matching system may inembodiments be adapted to facilitate matching a specified pattern ofillumination with any portion of the data in the images in the library,such as data from multiple two-dimensional images. In embodiments,pattern matching may account for patterns that result from illuminationof surfaces that are positioned at an angle from a light source, such asby specifying an off-axis slice through a portion of the data that isused to generate or characterize planar images of illumination patterns.

In embodiments, pattern matching may be based on determining, for agiven position in the near field illumination space (e.g., for a givendistance, and/or longitudinal and/or latitudinal orientation) whetherthe luminance data values in the library captured for at least one lightsource match those in the specified pattern. For example, a user mayseek a spot light that provides a substantially circular, three-footdiameter circle of light with an intensity level between a minimumthreshold and a maximum threshold at a distance of ten feet from thelight source, and the pattern matching system may filter light sourcesthat in the library to the subset that satisfy those requirements.Rather than having to match all the values, various matching thresholdsmay be devised to support matching similar patterns, such as a minimumnumber of matches in the pattern, a minimum number of near-matches inthe pattern e.g., values in the library being within a range of values(e.g., a tolerance band) relative to the pattern, and the like. In anexample of near-matching, a pattern may include a specific value for alocation in the pattern (for example, a value may be 0x92) plus a rangeof values that would satisfy a near-match criteria (e.g., +/−0x08) sothat any value in the location within the range of 0x84-0x100 may bedeemed to be a near-match. In embodiments, the pattern matching may bebased on a match tolerance value that is specified independently of thepattern to match. In embodiments, pattern matching may be based on atotal differential of values between the specified pattern andposition-corresponding values in a library. In embodiments of a patternmatching technique, a shape and set of values may be specified in thepattern. Any matching set of values in the library that conforms to theshape of the pattern (e.g., a rectangle, an oval, a circle and the like)may be selected. In this approach, a user may be interested indetermining the portion of the near field space of the light fixturethat matches the pattern. Other techniques for matching may be includedin the methods described herein including matching a hash and/orsignature value derived from the pattern to hash values generated forportions of the data in the near field luminance library, and the like.

In embodiments, generating a near field characterization of lightfixture luminance may include a user interface through which a user mayspecify and/or select a pattern of illumination for the space. The userspecified and/or selected pattern may be automatically provided as aninput to the pattern matching system.

In embodiments, near field luminance data may be processed with indirectmeasurement software to generate an area-source model of the lightfixture or source. An area-source model of the light fixture may takeinto consideration aspects of the fixture that may not be included in apoint-source model, such as may be obtained with far field data. Aspectssuch as differences in light radiating from different portions of thefixture may be modeled in this way.

In embodiments, generating a near field characterization of lightfixture luminance may include generating a three-dimensional volumetricluminance model from the two-dimensional images. A three-dimensionalvolumetric luminance model may be generated by arranging a plurality ofthe incremental distance differentiated images into a three-dimensionalshape, such that each of the images represents or corresponds to atwo-dimensional slice of the three-dimensional shape. Once such athree-dimensional volumetric luminance model is created, capturingluminance values and other lighting parameters at various points in 3Dspace around a light fixture, the model can be used to generate othertwo-dimensional slices, including ones that are different from theimages used to generate the model. Thus, the illumination cast by thefixture onto various surfaces (including flat and curved surfaces) canbe modeled in the three-dimensional volumetric luminance model, such asfor purposes of representing in a lighting design user interface theappearance that would be created by using a lighting fixture within agiven environment, including what illumination it would cast uponsurrounding surfaces. As further described elsewhere in this disclosure,illumination parameters may be maintained in the three-dimensionalluminance model that allow for modeling of interaction with surfacecharacteristics or other optical characteristics of the objects in anenvironment, such as for modeling the effect of color, reflection,absorption, refraction, and the like, of an object when impacted by theillumination provided by a light fixture at a given point and anglewithin the 3D space modeled by the model.

In embodiments, luminance values captured by the two-dimensional sensormay be converted to a measure of luminous flux including values for θand φ of the light source.

In embodiments, the near field luminance characterization may include θand φ luminous flux for each of a plurality of positions as well asx-coordinate and y-coordinate area image sensor location data. Thex-coordinate and y-coordinate image sensor location data may be mappedto a corresponding area location on the light source, based, forexample, on a distance from the sensor array, a position of the sensorarray, an angle of the sensor array relative to the light source, andthe like.

In embodiments, data representing the near field illuminationcharacterization in the library is dependent on at least one of: (i)distance between the light source and the surface or surfaces used forgeneration of the library, (ii) an angle between a line projected fromthe light source and a position on the surface(s) associated with one ofthe plurality of luminance values and a normal to the surface, (iii) oneor more optical properties of the surface(s), and (iv) the capturedluminance value associated with the position of the surface(s).

In embodiments, a system for capturing a near field illumination patterngenerated by a light source may include a positioning slide for holdinga screen and facilitating moving the screen among a plurality ofdistances from the light source. The positioning slide may also rotatearound the light source both longitudinally and latitudinally tofacilitate capturing light output from the light source at a range oflocations and distances. The system may also include an image sensorwith at least two-dimensions for capturing luminance values from atleast one side of the screen when the screen is illuminated by the lightsource. The system may also include a computer accessible digital datastorage system for storing a plurality of data structures, each datastructure representing the luminance values captured by the at leasttwo-dimensional image sensor at a given distance, and or position of thepositioning slide for a given light source. In embodiments, theplurality of data structures may be stored in a searchable library. Thesystem may further include a user interface through which a user maysearch for a light source having a desired pattern of luminance values,such as by specifying a desired pattern of luminance values and otherrelevant criteria about a desired light source.

In embodiments, a method of near field illumination pattern matching mayinclude capturing a plurality of two-dimensional images of anillumination effect in an environment illuminated by a light source. Themethod may include storing a portion of the plurality of images in adigital data structure that facilitates distinguishing among the storeddata values in each of the plurality of images by a two-dimensionallocation in an image of the plurality of images and an effectivedistance of the image from the light source. The method may also includedetecting a specified pattern of illumination of the environment in thedigital data structure. In embodiments, the pattern includes a pluralityof data values identified by a two-dimensional location value and lightsource distance value. To support detecting patterns that are off-axisrelative to the direction of light emanating from the light source, atleast two data values in the specified pattern may be located atdifferent light source distance values. Also, the light source distancevalue may vary across a portion of the specified pattern of illuminationto facilitate representing a pattern that may be non-planar. Theplurality of images may each be captured so that at least two of theimages are non-co-planar; however, the images may be substantiallyparallel. In embodiments, data in the digital data structure representsan impact of light from the light source on at least one object in theenvironment, such as a wall, column, furniture, floor, window, and thelike. Each of the plurality of captured images may be labeled with anidentifier that may facilitate referencing a captured image directly,such an identifier may be included in and/or referenced by the specifiedpattern of illumination.

Referring to FIG. 42 , an embodiment of a near-field characterizationsystem 4200 is depicted. An illumination capture system 4202 asdescribed herein may include a light positioning device 4202-1, anintermediate screen 4202-2 and an area array illumination sensor 4202-3.System 4202 may communicate with a capture illumination processingsystem 4204 that may process data from the illumination capture system4202 and store it as a data structure (NF1-1 and the like) in near fielddata structure library 4206. A pattern matching system 4208 mayinterface with the library 4206 and a user interface 4210 to facilitatematching at least portions of near field patterns as described herein.The pattern matching system 4208 may also facilitate identifyingcandidate fixtures that may produce patterns similar to a pattern to bematched. The pattern matching system 4208 may also interface with theuser interface 4210 through which a user may specify and/or select apattern for matching; view and/or select one or more fixtures that maybe presented in the user interface by the pattern matching system andthe like based on a similarity of patterns produced by the one or morelight fixtures with a pattern to be matched, such as one that the userhas specified and/or selected through the user interface 4210. The userinterface 4210 may further facilitate viewing an environment, such aswith one or more fixtures selected by the pattern matching system and/orthe user and the resulting near field light distribution from the one orfixtures in the environment. The user interface 4210 may usetwo-dimensional, three-dimensional, and/or virtual reality displaysystems.

Lighting Distributions: Bloom to Bloom Match

In embodiments, achieving consistency in lighting within an environmentor across a plurality of environments may be accomplished by ensuringthat light fixtures that provide consistent and/or desired lightingbloom effects are utilized. The term “bloom” is used herein to generallydescribe the illumination pattern emanating from a light fixture and/orbeing cast by a light fixture on surfaces and objects in its environmentand should be understood to encompass any of a wide variety of lightingcharacteristics (including shape, color, intensity, and the like)described throughout this disclosure, except where context indicatesotherwise. In embodiments, a design for lighting in an environment mayprescribe a certain bloom effect from a light source to be added to theenvironment. A desired bloom effect from a light source may be localizedto a portion of an environment in which the light source is deployed,such as a target area of illumination (e.g., a painting hanging on awall, an object, a walk way, a façade, and the like). Bloom effects froma light source may also be composed of bloom properties that helpcharacterize and distinguish among a range of bloom effects. However, abloom effect of a light fixture can be impacted by a range of factors,such as other light sources, bulbs used in the fixture, a shape and/ororientation of a shade, lens, mirror, or filter of the fixture, and thelike. Therefore, starting with a consistent bloom effect from a lightfixture may improve the chances of achieving a desired bloom effect.

In embodiments, determining a fixture that can produce a preferred bloomeffect may be accomplished by comparing a digital characterization ofthe preferred bloom effect to digital characterizations of known bloomeffects, such as those produced by other light fixtures. Therefore, asystem for matching bloom effects, such as one for facilitatingidentifying a light source based on a bloom effect may include a libraryof lighting objects that may represent lighting fixture objects and/orlight source objects. In embodiments, at least some aspect of a bloomeffect of the lighting objects can be accessed in the library. Inembodiments, a bloom effect may be generated from a light source model,such as a near field characterization of the light source and the like.Therefore, bloom effects for lighting objects in the library may begenerated and matched to desired bloom effects. In embodiments, modelsof lighting objects in the library that have lighting properties similarto properties of a desired bloom effect may be used to generate bloomeffects that may be used when selecting among (e.g., filtering, and thelike) candidate lighting objects in the library. The lighting objects inthe library may be characterized by lighting properties, such as outputbloom properties that characterize at least a portion of a luminancepattern provided by a lighting object selected from the library. Thislibrary may be accompanied by a pattern matching system that facilitatesmatching bloom effects, such as by matching bloom properties ofdifferent bloom effect stored in the library. In embodiments, thelibrary may include a collection of bloom effects represented by digitaldata structures of the bloom properties and the like. The bloom effectsin such a library may be associated with one or more light objects, suchas light fixtures, that may produce a specific bloom effect or a bloomeffect that is substantially close to a bloom effect in the library.Therefore, the pattern matching system may facilitate identifying atleast one lighting object in the library based on at least one outputbloom property. The pattern matching system may match bloom effects,properties and the like to facilitate matching a first (e.g., preferred)bloom effect to bloom properties in the library to determine a subset oflighting objects that produce a bloom effect similar to the preferredbloom effect. In embodiments, the pattern matching system may identifyjust one lighting object in the library that sufficiently matches thedesired bloom effect.

In embodiments, an output bloom property may describe a shape of anoutput bloom from the lighting objects. The output bloom shape may bespecified for a given distance from the lighting object, such as whenthe output bloom intersects with a surface of an object in theenvironment, such as a plane (e.g., wall, floor, door), column (e.g., avertical surface that may not extend as far as a wall, and a slope(e.g., a stairway, escalator, ramp, exterior sloped surface, and thelike). In embodiments, a shape of an output bloom, or the output bloomitself may be captured by a portion of a near field illumination patternor of a far field illumination pattern generated by a light objectselected from the library. The output bloom shape may be continuous,discontinuous, and the like. A given light fixture may produce a rangeof light blooms based on, for example, a type or wattage of bulb beingused in the fixture, a color of a shade, and the like; therefore, aplurality of blooms may be saved for each lighting object. Datadescriptive of the bloom, the lighting object, and the conditions thatimpact the bloom may be accessible in or through bloom-specific datasets. Determining if an output bloom of a lighting object matches to adesired bloom may include matching multi-dimensional data sets that eachinclude data for luminance proximal to a light source. A preferredoutput bloom may be represented by a three or greater dimensional dataset where at least a measure of light output is stored in cells of thedata set. A lighting object output bloom may also be represented by anear-field characterization of the light, that may also include a threeor greater dimensional data set where at least a measure of light outputis stored in cells of the data set. In embodiments, comparing bloomeffects may include comparing portions of near-field characterizationsof light sources.

In embodiments, light output bloom properties may include color,intensity, diffusion over distance, reflection from a surface in theenvironment, transmission through a surface in the environment, and thelike. A surface in the environment may be translucent. The surface maybe a shade of a lighting fixture, and the like.

In embodiments, the system for facilitating identifying a desired lightsource based on a bloom effect may include a user interface whereby auser can view and select a lighting object based on a display of theoutput bloom. The user interface may render to a user, via a computerinterface such as a virtual reality interface and the like, thepreferred output bloom effect in an environment, such as an environmentselected by the user. The user may select among lighting objects in thelibrary, and the output bloom for the selected lighting object may berendered. The user interface may further facilitate a user selectingand/or specifying a desired output bloom property, such as byreferencing a lighting object with certain conditions, identifying anexisting bloom with the property, entering the property directly (e.g.,a color or the like). The user interface may allow a user to view bloomeffects from, for example, a library of lighting object bloom effects inan environment and then select one as a preferred bloom effect. Forconditions that may impact the bloom if varied, such as ambient light inthe environment, the user interface may allow the user to adjust suchconditions.

In embodiments, the bloom pattern matching system of the system that mayfacilitate identifying a desired light source based on a bloom effectmay automatically identify at least one lighting object in the librarybased on a desired output bloom property, such as by matching an outputbloom property of the lighting object with the desired output bloomproperty. In an example, an output bloom property may include how abloom of illumination from a lighting object may illuminate otherobjects in the environment. Automation and bloom matching may beenhanced by performing artificial intelligence classification that maybe trained to match output bloom patterns based on a training set ofpatterns. Such a training set may be matched by one or more human users,and the like.

Near Field: Volumetric Renderer

In embodiments, a near field volumetric rendering facility mayfacilitate displaying a representation of lighting distributions from alight source in an arbitrary, three-dimensional environment. A renderingfacility that has access to near field light fixture data, which mayinclude data that represents illumination of a three-dimensional regionaround a light source, may apply light emission modeling techniques togenerate a rendering of light impacting an environment that correlatesto the near field functioning of the light fixture. In athree-dimensional space, rendering of near field data may includemodeling light source emissions as a set of light ray-traces. Inembodiments, near field illumination data may be stored as lightray-trace data. It may also be converted from a format that is not alight ray-trace data format into a light ray-trace data format formodeling. In an example, a near field data set that represents measuresof light luminance proximal to a light source may include data for arange of locations in the light source's near field. The data may beconfigured as a set of planar area segments of the near field space,where each segment represents the near field effect of the light sourceat a given distance in a given direction from the light source. Data inthis format, essentially may be represented as a three dimensionallyindexed array of light measures, may be converted into light ray-tracedata by, for example, selecting a ray that originates from a point onthe surface of the light source and ordering the values found in a cellthat the ray passes through in each of the area segments intersectedalong the path of the ray trace.

The modeled light emission set of ray-traces may represent light thattravels from a light source disposed relative to the three-dimensionalspace and that travels through the three-dimensional space to an elementin the three-dimensional space, such as a wall and the like. Themodeling may further include reflections of the light off elements inthe space. The reflections may be modeled based on a set of ray-tracesand at least one reflection characteristic of the element in thethree-dimensional space. In this way, if the surface is rough or matte,the reflection characteristic will result in a different effect of thelight than would a shiny, smooth surface. The modeled ray-trace data(emissions and reflections) may be converted and/or captured as lightvolume data. Any data in the volume that may be missing may beinterpolated based on, for example, nearby light ray-trace data valuesand/or nearby converted volume data values. The modeled data may then beprocessed to determine interactions among the ray-traces and reflectionsin the three-dimensional space. The interpolated data may be added tothe volume data, the ray-tracing data, and the like so that therendering facility may render the composite volume data, interpolateddata, and interactions among the ray traces in the three-dimensionalspace.

In embodiments, modeling may account for light transparency, absorption,reflection, refraction, filtering, and the like of elements in thethree-dimensional space. Modeling may further consider near fieldlighting artifacts, such as physical characteristics of the light source(e.g., a fixture shade, bulb orientation, stand, hanging chain,accessories such as snoots, barn-doors, cross hair baffles, celllouvers, screens, pattern templates, hoods, spread lenses, color lenses,and the like). Rendering may apply the modeled aspects, such as nearfield lighting artifacts, element features (e.g., transparency, and thelike) to the three-dimensional space so that the impact of these modeledaspects may be realistically rendered in the electronic view of thespace. Because the three-dimensional space may be a virtual space or areal space and may be presented as captured images or as live images,the near field volumetric rendering may be presented on a virtualreality display by, for example, interfacing with a virtual realitydisplay controller, an augmented reality display controller and thelike.

In embodiments, the light source may be made up of a plurality ofdistinct light elements, such as bulbs, and each light element may havea corresponding set of ray traces to be modeled. The data representingthe near field of the light source may include data for each lightelement. Alternatively, a near field data set for each light element maybe processed into one or more sets of ray-traces to be modeled. The nearfield volumetric rendering facility may render multiple sets of raytraces for the one or more light element so that interactions among theray traces in each set may be considered and presented in the rendereddisplay.

In embodiments modeling and/or rendering may account for distance-basedlight source intensity, so that, for example, a measure of lightintensity (e.g., brightness and the like) may be greater for a positionalong the ray-trace that is closer to the light source than for a distalposition along the ray trace. This distance-based light source intensitymay be captured in the ray trace data and/or may be generated duringmodeling, rendering, and the like. In an example of distance-based lightsource intensity modeling and/or rendering, light source intensityfall-off over distance from the light source for each ray-trace in theset of ray-traces may be presented in the resulting rendered display ofthe three-dimensional space.

In embodiments, various techniques for capturing light emissions to bemodeled may be employed. One exemplary technique includes disposing asurface at a plurality of positions with different distances from thelight source along a ray-trace path and capturing at least luminancevalues of the light interacting with the surface at each of the disposedpositions. The collected luminance values may, for example, be a set oftwo-dimensional values for each disposed surface position. The collectedluminance values may be stored as two-dimensional image slices of aportion of a near field space of the lighting fixture, effectivelyresulting in a three-dimensional collection of near field data, whereeach two-dimensional image slice may be associated with a uniquedistance along the ray-trace from the light source so that each datavalue captured in each slice may represent a three-dimensional locationin the near field of the light source. By repeating the disposing,capturing, and storing steps, a volumetric representation of the nearfield of the light source may be produced for modeling and rendering.

In embodiments, a method of electronic display rendering of a lightingdistribution in a three-dimensional space may start with capturing aplurality of two-dimensional images of at least one of light sourceemissions and reflections of light originating from a light sourcedisposed in an environment. The method may continue by storing a portionof the plurality of images in a digital data structure as lightvolume-data. The structure may be adapted to facilitate distinguishingamong the light volume data in each of the plurality of images by atwo-dimensional location in an image of the plurality of images and aneffective distance of the image from the light source, essentially athird dimension. Light emissions and reflection for positions in thisvolume data that are not directly captured may be interpolated. Theupdated volume data, as well as any detected reflections (that may beincluded in the volume data) may be modeled as a set of light ray-tracesthat represent light traveling from a light source to an element in thethree-dimensional space. Modeling reflections may be based on the set ofray-traces and at least one reflection characteristic of the element inthe three-dimensional space. The modeled volume data, interpolatedpoints, reflections, and interactions among the ray-traces may berendered for display in an electronic display of the environment, whichmay be an arbitrary three-dimensional space.

In embodiments, modeling and/or rendering may account for lighttransparency, absorption, reflection, refraction, filtering, and thelike of elements in the three-dimensional space. Modeling may furtherconsider near field lighting artifacts, such as physical characteristicsof the light source (e.g., a fixture shade, bulb orientation, stand,hanging chain, and the like). Rendering may apply the modeled aspects,such as near field lighting artifacts, element features (e.g.,transparency, and the like) to the three-dimensional space so that theimpact of these modeled aspects may be realistically rendered in theelectronic view of the space. Because the three-dimensional space may bea virtual space or a real space and may be presented as captured imagesor as live images, the near field volumetric rendering may be presentedon a virtual reality display by, for example, interfacing with a virtualreality display controller, an augmented reality display controller andthe like. In embodiments, an experience with near field volumetricrendering may be enhanced through the use of a virtual realitycontroller, and the like.

In embodiments, the light source may be made up of a plurality ofdistinct light elements, such as bulbs, and each light element may havea corresponding set of ray traces to be modeled. The data representingthe near field of the light source may include data for each lightelement. Alternatively, a near field data set for each light element maybe processed into one or more sets of ray-traces to be modeled. The nearfield volumetric rendering facility may render multiple sets of raytraces for the one or more light element so that interactions among theray traces in each set may be considered and presented in the rendereddisplay.

In embodiments modeling and/or rendering may account for distance-basedlight source intensity, so that, for example, a measure of lightintensity (e.g., brightness and the like) may be greater for a positionalong the ray-trace that is closer to the light source than for a distalposition along the ray trace. This distance-based light source intensitymay be captured in the ray trace data and/or may be generated duringmodeling, rendering, and the like. In an example of distance-based lightsource intensity modeling and/or rendering, light source intensityfall-off over distance from the light source for each ray-trace in theset of ray-traces may be presented in the resulting rendered display ofthe three-dimensional space.

Referring to FIG. 43 , an embodiment of volumetric rendering isdepicted. Volumetric rendering, which may be described herein, mayinclude capturing with an area array sensor 4302 ray traces 4304,reflections 4306, and interactions among ray traces and reflections 4308for light being transmitted from a light source 4301 in an environmentincluding features such as a desk 4310, wall 4312 and the like. Thecaptured ray trace and reflection day may be processed to interpolate4314 missing data. The captured and interpolated data may be stored in astorage facility for light volume data 4316 from which a light volumemodeling facility 4318 applies the captured light volume data 4316 withelement light interaction properties 4320 to produce ray trace data 4322that a volumetric rendering facility 4324 turns into a visualrepresentation of a space being illuminated by the light source 4301 anddisplayed in a virtual reality or the like display 4326.

Color: Legacy Control of Programmable Curves

In embodiments, lighting fixtures may be controllable for color andintensity independently, such as through separate control inputs. Inembodiments, control inputs may be based on a voltage input range, wheredifferent values within the range map to different colors andintensities. It may be desirable to provide a control function for suchlighting fixtures that can provide coordinated control of these separateinputs so that the impact of changing one (e.g., brightness via dimming,for example) on the other (e.g., color of a light will typically changedue to the intensity of light being produced) can be mitigated oreliminated. Additionally, a control protocol, which may be a tuningprofile or the like may be configured to emulate a legacy light bulbbrightness versus color performance, such as an incandescent bulb, gaslamp, compact fluorescent and the like. A control input range, which maybe a range of voltage placed on an input to the light element may bemapped over any range of color or brightness. In an example, a fixedrange, such as over 10 volts, may be mapped in a first color curve toadjust color from 2000K to 2900K, whereas a second color curve may mapthe range of 10 volts to adjust color from 1700K to 4500K. In anexample, an input voltage range for dimming a light in a specific scenemay be mapped to a one-volt range (e.g., 2V-3V) and the like. This maylimit the range of light intensity to a subset of the full range ofcontrol possible with the light. In embodiments, mapping a dimming rangefor a specific scene to 2V-3V may facilitate interfacing with devicesthat have limited voltage output capability. In this example, a controldevice that is limited to 3V maximum output could be used withoutlimiting the desired degree of dimming. In each case, both the range ofthe curve and the individual points may be customized to provide adesired transition of color as input proceeds over the voltage range.

Through the use of different, customizable color curves, a range oflight performance can be accomplished for color programmable lights. Inembodiments, a color curve may be configured to support human biologicalrhythms, such a circadian rhythm and the like. A cool color curve may beconfigured at the start of a day to produce a range of cool color lightto encourage wakefulness and activity. The same programmable light maybe configured with a warm color curve that produces a range of warmcolors in the evening to promote restfulness. By configuring a customcolor curve for a programmable light, a legacy controller, such as aconventional dimmer, may be used while still enabling the desired outputcolor type or effect. In embodiments, color curves may be configured tosupport non-visual light such as ultraviolet, special purpose light suchas plant biologic, ultraviolet for sterilization, infrared for security,and the like.

In embodiments, enabling custom tuning, such as color and/or brightnessof a lighting object may be performed by various methods includingdefining a custom tuning profile and, under control of a processor,controlling one or more lights to comply with the tuning profile. In anexample of such custom tuning, the custom tuning profile may specify acolor tuning profile, a dimming profile, a light distribution profile,and the like for a lighting object to be controlled. In the example, theprocessor may translate the custom tuning profile into a set ofinstructions, such as a range of voltages, for controlling the lightingobject to behave per the profile. User input, such as through a dimmer,via voice input, and the like may be accepted by the processor and usedas a guide to determine which portion of the tuning profile to follow.In embodiments, the custom tuning profile may be a dimming profile thatspecifies a set of points on a color temperature gamut that defines adiming curve along which the light object will dim in response to acontrol input, such as a dimmer. In this way, adjusting a dimmer willcause a change in the color output of the light object, such as toachieve a desired color for a given light output. As noted above, thedimming profile may be specified to match a legacy or other knowndimming profile of a type of lighting object, such as an incandescentlight, a gas light, a halogen light, and many others. In embodiments,the custom tuning profile may be a color tuning profile that specifies aset of points on a color temperature gamut that defines a color curvealong which the light object will adjust in response to a control input,such as a variable voltage control input. In this way, adjusting thevoltage input will cause a change in the color output of the lightobject, such as to achieve a desired color for a given light output.

In embodiments, a user interface may be employed to facilitate a userdefining a custom tuning profile. A user may specify a custom dimmingprofile by tracking curve on a brightness gamut. A user may, forexample, specify a custom color tuning profile by tracking a curve on acolor gamut. A user of the user interface may, for example, select aninput value, such as a maximum or minimum input control voltage, andselect a color from the gamut to apply to the selected control inputvoltage.

In embodiments, a library of stored profiles may be available to a userwhen configuring a custom tuning profile. A user may, such as throughthe user interface, select a stored profile from the library. Storedprofiles may include at least one of a color quality profile, acircadian profile, a concentration profile, a relaxation profile, anefficacy profile, a security profile, and the like. The library may alsobe organized to profile custom tuning profiles that satisfy aperformance requirement, such as energy savings, an aestheticrequirement, such as avoiding blue light, and the like.

In embodiments, lighting objects with independent brightness and colorcontrol variable voltage inputs may be configured to satisfy a preferredcolor performance by referencing a custom tuning profile, such as acolor curve and assigning the color curve to a fixed voltage controlinput range for the color control input so that each incremental voltagevalue applied to the input will result in a color specified on the colorcurve by mapping the color curve to the fixed voltage range. Inembodiments, the custom color curve may be a dimming profile thatspecifies a set of points on a color temperature gamut that defines adimming curve along which the light source will dim, so that changes toa dimming control input will cause a coordinated color output from thelight. The coordinated color output from the light may be a consistentcolor. To achieve this consistent color over a range of dimming controlvalues, the color voltage control values may be adjusted accordingly.The dimming profile may be selected to match an existing light object,such as an incandescent bulb and the like.

In embodiments, the custom tuning profile may be a color tuning profilethat specifies a set of points on a color temperature gamut that definesa color curve along which the light object will adjust in response to acontrol input, such as a variable voltage control input. In this way,adjusting the voltage input will cause a change in the color output ofthe light object, such as to achieve a desired color for a given lightoutput.

In embodiments, a library of stored profiles may be available to a userwhen configuring a custom tuning profile. A user may, such as throughthe user interface, select a stored profile from the library. Storedprofiles may include at least one of a color quality profile, acircadian profile, a concentration profile, a relaxation profile, anefficacy profile, a security profile, and the like. The library may alsobe organized to profile custom tuning profiles that satisfy aperformance requirement, such as energy savings, an aestheticrequirement, such as avoiding blue light, and the like.

In embodiments, legacy control of programming profiles may beaccomplished by a light source control system for a light withindependent color and dimming control inputs may include a first outputport that is operatively coupled to a color control input of a lightsource and a second output port that is operatively coupled to a dimmingcontrol input of the light source. The system may further include aprocessor sub system that accesses a light source tuning profile thatcharacterizes a multi-dimensional lighting curve by mapping color outputof the light source to brightness of the light source so that a changein the brightness input causes a coordinated change in the color inputbased on the curve. In embodiments, the processor controls both thefirst and second outputs based on information in the tuning profile, sothat changing the brightness input results in the processor alsochanging the color input to adjust the color of the light based on thetuning profile. In an example of coordinated input control, controllingthe dimming control input to reduce the brightness causes a coordinatedchange in color control input that results in a warmer color beingoutput by the light. Similarly, increasing the brightness results in acooler color being output by the light.

In embodiments, the tuning profile may map a plurality of target colorand brightness output values to a corresponding plurality oftwo-dimensional voltage values, a first dimension controlling lightcolor and a second dimension controlling brightness of the light source.The profile may map values in the first dimension to a color controlinput voltage range. The profile may map values in the second dimensionto a brightness control input voltage range. The tuning profile may maptarget output color temperatures of the light source to values in thefirst and second dimensions so that controlling the color input andbrightness input based on the first and second dimensions configures thelight source to output a target color temperature based on the tuningprofile color temperature mapping. A two-dimensional mapping of thetuning profile may facilitate maintaining a light output color as thelight is dimmed by, for example, adjusting the light color input controlvoltage based on a change in the light dimming control input voltage.

In embodiments, the tuning profile may be indexed by at least one ofbiologic impacts and physiological impacts of light so that at least oneof the light color and the light brightness is specified for a pluralityof biologic impacts and physiological impacts. This may facilitate auser selecting a tuning profile that has a preferred biologic impactthroughout a control range, such as if the user were to dim a lightunder control of this profile, the resulting color would comply with thepreferred biologic impact.

Referring to FIG. 44 , an embodiment of legacy color programmablecontrol is depicted. A light controller 4402 accesses a data set ofcustom tuning profiles 4403 and a user control 4404, such as a dimmer tocontrol light emissions 4410 from a light source to provide legacycontrol of light color 4406 and brightness 4408 using a custom tuningprofile. Light source control inputs for controlling, for example, lightcolor 4406 and light brightness 4408 may be mapped via a custom tuningprofile to adjust color and brightness of a light source over customranges. In the embodiment of FIG. 44 , a first custom color tuningprofile 4416 may map a portion of the entire color spectrum 4412 to thefull input 4406 control range to adjust the color output from the lightsource from approximately 1700K to 6500K. A second custom color tuningprofile 4414 may map the full input 4406 control range to adjust thecolor output from the light source from approximately 3300K to 10000K.Depending on the custom color tuning profile selected, the controllerwould be configured to control the light color output over the customtuning range of color. Likewise, a custom dimming profile 4418 may map aportion of the entire brightness capability of the light 4410 to acustom subset that is slightly less than the full range. In thisembodiment, adjusting the dimming dial 4420 through its full range wouldcause light output from the light source to range from nearly dark (butnot fully off) to nearly maximum brightness (but not fully bright). Inembodiments, either of the color tuning profiles 4414 and 4416 may becoordinated with the custom dimming profile 4418 so that an adjustmentof the dimmer 4414 may cause a coordinated change in the color controlinput 4406 to, for example, substantially maintain the color output fromthe light source for a range of brightness values in the custom dimmingprofile 4418.

E: Main: Emotional Filter

In embodiments, emotional filters may be useful for lighting design.Filters that relate to stylistic, aesthetic, and perceptive aspects oflighting in an environment may provide value to a lighting designprocess. In embodiments, an exemplary technique for using emotionalfilters may include configuring, populating, maintaining, and using anemotional content data structure for emotional filter-relatedinformation about light sources, environments, and the like. Emotionalcontent information about an environment may be captured from processinga live capture of the environment with an emotional filter featurecapture facility. An emotional filter feature capture facility may alsoprocess a visual representation of an environment, such as a livestream, a set of one or more still images (e.g., photograph), a video ofthe environment, a text description of the environment, and the like.

In embodiments, using emotional filters in a lighting design process mayinclude capturing stylistic, aesthetic, and other features from a visualrepresentation of an environment, such as a room in a private home, anoffice, exterior space, and the like. Capturing features may includeanalyzing at least one of images, 3D models, renderings, and scans ofthe environment. The captured emotional filter-related features maybepopulated in an installation-specific instance of an emotional contentdata structure that may provide structured access to emotionalfilter-related data suitable for use in lighting design. Populating mayinclude storing at least one of cultural and geographical dataassociated with the environment in the installation-specific emotionalcontent data structure. Over time, feedback about emotional aspects ofinstallations, such as physical installations, virtual renderings ofinstallations, and the like that may be characterized by aninstallation-specific emotional data structure may be captured. Thefeedback may be processed by machine learning algorithms to develop anunderstanding of factors that contribute to each of a plurality ofemotional effects. The processed feedback may then be used to updateportions of the installation-specific instance of the emotional datastructure. Emotional content data structures for lighting fixtures usedin the environment for which feedback is captured may also be updatedbased on the feedback. Feedback may include quantitative and qualitativedata that may impact data items in the emotional content data structurefor the environment and/or for the lighting fixtures, if any, deployedin the installation-specific instance.

In embodiments, using emotional filters in a design lighting process mayinclude selecting at least one light source for the environment based ona similarity of a portion of an emotional content data structure for thelight source with a corresponding portion of the installation-specificemotional content data structure.

In embodiments, the emotional content data structure may be configuredto support objects, classes, and properties including lightingproperties, such as distribution of light on lighting space objects,distribution of lights on surfaces, illumination values, color and colortemperature of light sources, spectral content, fixture type, and thelike. Lighting space objects may include any object in the environment,such as, without limitation desks, tables, appliances, drapery, walls,columns, doors, staircases, furniture, vehicles, toys, televisions, andthe like. In embodiments, spectral content may include quality andintensity of light at certain spectral ranges. In embodiments, fixturetype may include any of a wide range of fixture types including, withoutlimitation modern, retro, industrial, romantic, suspended, embedded, andthe like.

In embodiments, a lighting design system using emotional filters mayinclude a display, such as a computer display, virtual reality display,augmented reality display, 3D display and the like for presenting avisual representation of an environment. The visual representation mayinclude a photograph, live stream, video, and the like and may beanalyzed by a feature capture facility adapted to capture stylistic andaesthetic features of the environment from the visual representation.The feature capture facility may be adapted to capture stylistic andaesthetic features by analyzing at least one of images, 3D models,renderings, and scans of the environment. The system may also include aninstallation-specific emotional content data structure that isaccessible to a processor into which the captured features arepopulated. The system may employ machine learning algorithms executingon a processor to receive user feedback about emotional and/or aestheticaspects of an installation characterized by the installation-specificemotional content data structure, thereby generating an understanding offactors that contribute to each of a plurality of emotional effects.This feedback may be used to update at least a portion of the emotionalcontent data structure of the environment and/or of lighting sourcesdeployed in the environment.

In embodiments, the system may include a light source selection facilitythat may facilitate identifying at least a candidate set of lightsources (e.g., fixtures, and the like) for satisfying emotional featuresof the environment based on similarity of a portion of an emotionalcontent data structure for light sources with a corresponding portion ofthe installation-specific emotional content data structure. Theemotional data structure may support storage of cultural and/orgeographical data. Such data associated with the environment may bestored in the installation-specific emotional content data structure. Inembodiments, the emotional content data structure may support, withoutlimitation objects, classes, and properties including lightingproperties selected from a group consisting of distribution of light onlighting space objects, distribution of lights on surfaces, illuminationvalues, color and color temperature of light sources, spectral content,fixture type, and the like. Examples of lighting space objects include,without limitation desks, tables, appliances, drapery, walls, columns,doors, staircases, furniture, vehicles, toys, televisions, and the like.In embodiments, spectral content may include quality and intensity oflight at certain spectral ranges. In embodiments, fixture type mayinclude any of a wide range of fixture types including, withoutlimitation modern, retro, industrial, romantic, suspended, embedded, andthe like.

In embodiments, a system for emotional filter-based lighting design mayfurther include a library of light source emotional content datastructures that describe stylistic and aesthetic features of a pluralityof light sources. The system may also include a light source selectionfacility that compares at least one portion of emotional content datastructures in the library with a corresponding at least one portion ofan installation-specific emotional content data structure therebygenerating a set of candidate light sources for satisfying at least oneof aesthetic and stylistic aspects of the environment. In embodiments,information descriptive of the aesthetic and/or stylistic aspects of theset of candidate light sources may be displayed on an electronic displayto enable comparison with each other and with aesthetic and/or stylisticaspects of the environment.

Near Field: Hardware—Manifestations of the Hardware

In embodiments, capturing light from a light source may be used tocharacterize a near field illumination of the light. Near fieldillumination capture may be performed with equipment that enableslocating a light source, and at least an illumination-sensitive sensor(e.g., a camera and cover screen combination and the like) at aplurality of known locations and orientations relative to each other. Agoal of capturing near field illumination is to capture the illuminationat a wide range of locations, including different distances,longitudinal and latitudinal positions, and the like in the near fieldof the light. A near field characterization system may include a lightpositioning device, a light filtering device, a light capture device,and a processing system for processing and storing the captured lightdata so that it can be used in algorithms that help characterize nearfield illumination effects of the light source. In embodiments, a nearfield characterization system may include a light source positioningsupport that may be adapted to hold a light source (e.g., a lightfixture and the like) disposed in one of a plurality of orientations tofacilitate distributing light in the near field at least to otherelements of the system. The system may also include an intermediatescreen that may be disposed at one of a plurality of intermediatepositions (e.g., between a light source and a light sensor array) in thenear field. The screen may be disposed so that a first side receives thedistributed light. The screen may be constructed to transfer a portionof the received light to a second side that may be substantiallyparallel to the first side (e.g., the intermediate screen may be atleast two-sided and at least partially translucent). The system mayinclude a two-dimensional array illumination sensor that may bedisposed, such as in the near field, to capture an image of the screen,preferably the second side of the screen. The captured illuminationimage may include data values representing illumination captured at eachof a plurality of sensing elements distributed substantially uniformlyacross the two-dimensional array. Each of the plurality of sensingelements may be queried to provide a digital representation of thecaptured illumination. The system may also include a processor that maybe adapted to control the illumination sensor and store the capturedillumination data value and the location (x and y) within the array. Thex and y location may correspond to a pixel or other type of individualsensing element in the array. In embodiments, there may be “x times y”sensing elements in the array. The system may further include a datastorage facility that works with the processor to facilitate storing thedata value and a corresponding array position for a plurality of imagesensing positions in the array. Additionally, the data storage facilitymay store information descriptive of the data value, such as itsrelative location in the array (x and y value), position of the array,orientation of the array, distance of the array relative to the screenand/or to the light source, an index that identifies the data value andall other data values in the array as an image group, a date/time stampof capturing the data, ambient light conditions, whether an enclosurewas employed, and the like. In embodiments, control of the illuminationsensor may include resetting the sensor, such as between capture events,accessing the data stored in the individual elements of the sensor,adjusting an exposure mode of the sensor, configuring the sensor, suchas for adjusting for ambient light, and the like, positioning thesensor, rotating the sensor, changing an orientation of the sensor, andthe like. In embodiments, the two-dimensional array illumination sensormay be a digital camera sensor, such as a digital camera feature of asmartphone and the like. In embodiments, the intermediate screen may betranslucent so that light impacting the first side is visible on thesecond side. In embodiments, the intermediate screen may be patterned sothat a pattern of the light impacting the first side is visible on thesecond side. In embodiments, the intermediate screen may be an activescreen, such as an LCD screen and the like that can be adjusted by aprocessor to cause a controlled amount of light to pass from the firstside to the second side.

In embodiments, the system may include a positioning system of the lightsource, the intermediate screen, or the sensor array that may, underprocessor control configure these three elements into a plurality ofdifferent relative orientations. In embodiments, a processor controlledlight source positioning system may facilitate fixing the position ofthe screen while adjusting the distance of the light source from thescreen, and a position of the light source in any longitudinal andlatitudinal position (e.g., any spherical position), including rotationand translation. Likewise, a processor controlled intermediate screenpositioning system may vary the distance and spherical position of thescreen relative to the light source. Additionally, a processorcontrolled illumination sensing array positioning system may vary thedistance and spherical position of the screen relative to the lightsource. In embodiments, the intermediate screen may be optional so thatonly the light source and sensor array positions would need to becontrolled.

In embodiments, a near field light source characterization system mayinclude an enclosure that mitigates an impact of ambient light, or otherlight sources, on the intermediate screen and the area arrayillumination sensor.

In embodiments, a near-field light source measurement andcharacterization system may include a processor controlled light sourcepositioning support adapted to hold a light source disposed todistribute light in a plurality of orientations. The processor maycontrol the rotation of the light source about a longitudinal axis ofthe support. The processor may control the rotation of the light sourceabout a latitudinal axis of the support. The processor may controltranslation of the light source in any of three directions. Thenear-field light source measurement and characterization system mayinclude a two-sided intermediate screen that has a first side and asubstantially parallel second side. In an exemplary deployment of thesystem, the intermediate screen may be positioned relative to the lightsource to receive the distributed light on the first side and it may beconstructed to transfer a portion of the received light to the secondside, such as by passive means, such as being translucent and the like.The near-field light source measurement and characterization system mayalso include an area array illumination sensor disposed relative to thescreen to capture light emissions from the second side of theintermediate screen. The near-field light source measurement andcharacterization system may also include a controller that may beadapted to control the illumination sensor and store the data value, thearray location of the corresponding image sensing position, andinformation descriptive of the sensing event in a data storage facility.The near-field light source measurement and characterization system mayfurther include a housing that mitigates the impact of ambient light onthe elements of the system so that ambient light can be ignored whendetermining a characterization of the near field illumination of thelight source. In embodiments, the housing may extend from the secondside of the intermediate screen to the array sensor. In embodiments, thehousing may enclose the light source, the intermediate screen and thearea array sensor. In embodiments, the housing may be configured toconditionally eliminate ambient light from reaching the enclosed systemelements, such as through the use of a door, adjustable screen or shade,active housing elements, such as LCD-based enclosure segments, and thelike.

In embodiments, the near-field light source measurement andcharacterization system may further include a spectrometer disposedrelative to the intermediate screen to capture the spectral content oflight proximal to the intermediate screen. When the spectrometer isdisposed between the light source and the screen, spectral content oflight being distributed by the light source may directly be captured bythe spectrometer. When the spectrometer is disposed between the screenand the area array sensor, spectral content of light passing through theintermediate screen may be captured by the spectrometer. In embodiments,the spectrometer may be moved between these positions to capturespectral content on both sides of the intermediate screen to facilitateanalysis of the spectral filtering impact of the screen, and the like.

In embodiments, operating the near-field light source measurement andcharacterization system to capture near field data may includerepeatedly adjusting the position of at least one of the screen and/orthe area array sensor relative to the light source while capturingillumination data at each adjusted position until enough data iscaptured to enable characterization of at least a portion of the nearfield of the light source. The increment of adjustment may be based onfactors, such as the size of the light source, the size of the arraysensor, the optical properties of the array sensor, a specified degreeof resolution of the near field data, and the like.

In embodiments, characterization of the near field of the light sourceby the near-field light source measurement and characterization systemmay include calculating an effective distance between the light sourceand the area array. Such an effective distance may be calculated as alogarithm of the physical distance. In embodiments, effective distancevalues may be applied to functions that determine, for example, afall-off of luminance for a range of effective distances.

In embodiments, successive captures of illumination may be performedwith varying relative orientations, positions, and distances of thelighting source and the near field illumination capture elements (e.g.,the intermediate screen and/or the array sensor, and the like). Thevariations for any of position, orientation, distance, and the likebetween captures may be non-linear, such as logarithmic and the like. Inembodiments, an increment of any of the relative position, orientation,distance, and the like may be determined for each successive capture. Inembodiments, the increment may be determined based on an entropy or anamount of information captured in a prior capture event, such as theimmediately prior event, a capture event earlier than the immediatelyprior event, a calculation of multiple prior capture events (e.g., anaverage of two or more capture events), and the like. Therefore, for anygiven pair of increments between successive captures, the pair ofincrements may be linear or non-linear based on the incrementdetermination.

In embodiments, the intermediate screen and the area array sensor may beintegrated so that movement of either causes a corresponding movement ofthe other. In embodiments, the screen may be an attachment to asmartphone camera feature so that for any orientation of the smartphone,the relationship between the screen and the camera remains unchanged. Inembodiments, the screen may be a universal smartphone camera featureattachment that can be adapted by a user to conform to a range ofsmartphone enclosures. Such a screen adapter may be configured toeliminate ambient light from the region between the screen and thesmartphone camera feature.

Near Field: Software—Reconstructive Algorithms—Iterative or Otherwise

In embodiments, characterizing a near field illumination generated by alight source, such as a light fixture and the like, may be performedwith one or more light sensing devices disposed proximal to the lightsource and a set of reconstructive algorithm(s) that can take theinformation from the one or more light sensing devices, such as amulti-dimensional (e.g., two-dimensional) set of image sensors (e.g., anarray of image sensors, one or more cell phone cameras, and the like)and produce a three-dimensional data set of values that represent theillumination produced by the light source at a plurality of locations(e.g., distances and directions from the light source) in the near fieldof the light source. Reconstructive algorithms may work, for example, ona collection of two-dimensional data points, such as a set of imagesthat capture the illumination at multiple locations (e.g., image planes)within a volume proximal to the light source. Reconstructive algorithmsmay also operate iteratively, constructing and updating athree-dimensional near-field data set for a light source as additionaldata (e.g., from two-dimensional planar images) is made available. Inembodiments, while the data may be termed two-dimensional orthree-dimensional, these dimensions merely refer to the location withinthe near-field at which illumination data from the light source iscollected (i.e., distances and directions in a coordinate system). Thedata values themselves may be multidimensional, including aspects suchas color, intensity (e.g., brightness), color temperature, saturation,and the like, including any of the characteristics attributed to lightsources, lighting objects, and the like as described throughout thisdisclosure.

In embodiments, an exemplary method for characterizing a near fieldillumination effect of a light source by applying a reconstructivealgorithm to a collection of two-dimensional illumination data sets(e.g., two-dimensional images) may include iteratively capturing theillumination data with, for example, a two-dimensional array imagesensor so that each of the image sensor elements in the array capturesat least one aspect of illumination, such as intensity, brightness,color, color temperature, saturation, and the like. Each imageiteratively captured may be differentiated by at least one of a distancefrom and a relative position (e.g., direction) of the two-dimensionalsensor array relative to the light source. Information that facilitatesdetermining position, and orientation, such as longitude and latituderelative to the light source for each two-dimensional image, or adistance and angle in polar coordinates, may be used by thereconstructive algorithm to produce a data set that represents theillumination of the light source at a set of points within a volume. Inembodiments, a reconstruction algorithm may generate a data set thatlinks one or more values in one or more two-dimensional images with oneor more other values in one or more two-dimensional images to facilitateprocessing the resulting volume data (e.g., as ray-trace data and thelike). In embodiments, the resulting volume data set output by thereconstructive algorithm may include a multi-dimensional representationof the near-field illumination of the light source. The dimensions mayinclude a first dimension of the two-dimensional array, a seconddimension of the two-dimensional array (e.g., x and y values of thearray), a theta component of the illumination value, a phi component ofthe illumination value at each x/y location, a value representing thedistance from the light source, a longitude value and a latitude value,and the like.

In embodiments, a reconstructive algorithm may determine a relativecontribution of each point source of a light source (e.g., eachindividual bulb, lens, and the like) such as on a surface of a lightfixture to each location in the volume of near field data.Reconstructive algorithms, and the like, for producing amulti-dimensional near field data set that may include methods such asKaczmarz, numerical, machine learning, neural networks, linear algebraand the like. In embodiments, data captured and used to generate amulti-dimensional near field data set of a light source may result in ahigh-fidelity model of the light source. In embodiments, areconstructive algorithm may make use of, or be used to facilitategeneration of, a model of the illumination pattern from a light source,such as where a model recognizes the presence of two (or more) pointsources (e.g., a pair of bulbs or LEDs) in a lighting fixture thatproduce different lighting effects in different directions from thelighting fixture by virtue of variations in the mixing/interference ofthe light from the point sources at different angles from the lightingfixture. A model, such as a ray trace model, may be constructed by whichthe presence of different light sources within a lighting fixture may beunderstood and based which values of illumination may be interpolatedfor locations within a three-dimensional space around a lighting fixturefor which measured data does not yet exist. Such a model may beiteratively updated, such as using machine learning techniques, asadditional data is acquired, such as using the image sensor setsdescribed herein. A collection of such models may be generated andstored by the system, such as to allow recognition of common patterns,such as pairs of bulbs, triplets, and the like, thereby allowinggeneration of three-dimensional representations of the illuminationaround a light source that represents a combination of measured datafrom image sensor sets (e.g., two-dimensional arrays) and data that isestimated, such as by interpolation, from a model.

In embodiments, a relative position of a light source, a sensor arrayand other elements used to capture near field illumination data may becontrolled by a processor. Aspects such as distance, orientation,location in three dimensions, exposure time, and the like may becontrolled. When an intermediate screen is used to facilitate indirectillumination capture, the reconstructive algorithms may process lightpattern data that is visible on a side of the screen opposite the lightsource.

In embodiments, incrementally reconstructing a near field illuminationeffect of a light source may use reconstructive algorithms as describedherein adapted to incrementally develop a three-dimensional (e.g.,volume-based) set of data for a three-dimensional region proximal to thelight source. Such an approach may include capturing a firsttwo-dimensional image with an image sensor disposed at a first locationrelative to the light source. Upon capturing a second image at a secondlocation (e.g., a further distance from the light source) thereconstructive algorithm may process the two images with their positiondata to produce a three-dimensional data set of the near fieldillumination of the light source. As successive two-dimensional imagesare captured, the reconstructive algorithm may incrementally enlarge thethree-dimensional data set based on the position of the newly capturedimage. This process can be repeated for a full range of distances,longitudes, latitudes and the like throughout any portion of thespherical volume proximal to the light source. In embodiments, thecaptured data values may include luminance values for theta and phi aswell as data values representing brightness, color, and the like. Inembodiments, producing a three-dimensional space data set of near fieldillumination may include processing two-dimensional images captured at aplurality of distances for a given longitude and latitude, imagescaptured at a given distance and longitude but with incrementallyadjusted latitudes, images captured at a given distance and latitude butwith incrementally adjusted longitudes, and the like. Examplesreferencing longitudes and latitudes should be understood to encompasssimilar concepts of distance and direction as expressed in othercoordinate systems, such as X-Y coordinates, polar coordinates, and thelike. Similarly, various mathematical/geometric transformations frompoint data collected in one coordinate system (e.g., a two-dimensionalimage sensor) to volumetric data in another coordinate system (e.g.,polar coordinate data for a volume around a light source) should beunderstood to be encompassed herein. Other combinations of distance,longitude and latitude may be incrementally captured and processed.

In embodiments, reconstructive algorithms may process data captured byvarious devices including without limitation an indirect luminancecollection device that includes a smartphone camera adapted to captureillumination from the light source indirectly. In such embodiments, thesmartphone may be adapted with a screen or filter, such as attached tothe smartphone over the smartphone camera or disposed to reflect lightto the smartphone camera, so that light from the light source impactsthe smartphone camera indirectly. Other arrangements involving indirectillumination (such as by a pass-through filter or a reflective screen)are encompassed herein, including ones that use various types of imagesensors, including CMOS sensors.

Referring to FIG. 45 , a flow diagram depicting incremental light modelgeneration using an iterative image capture and regenerative algorithmas described herein. An initial position 4502 of a luminance sensor isused in an image capture process 4504 that captures, such as through anindirect near field data collection process described herein an initial2D image 4508 of luminance from a light source. The initial position4502 is adjusted, a second image capture process 4504′ is performed anda second image 4508′ is captured. The two captured images 4508 and 4508′are processed by a regenerative near field lighting model algorithm 4510that produces, for example a first-generation light model 4512, such asa near field characterization model of the light source. Successiveadjustments to position and/or orientation are made along with capturesof corresponding 2D images (e.g., 2D Image 4508″ and the like). Theregenerative algorithm 4510′ is applied for each acquired image toproduce successively richer lighting models, ultimately producing amodel that is configured to emulate a portion of a near field luminancevolume space of the light source after the final image 4504″ is capturedand processed.

Referring to FIG. 46 , a flow diagram depicting incremental imagecapture and aggregated image processing by a regenerative algorithm thatproduces a near field volume luminance model of a light source. Aninitial position and/or orientation of a luminance capture device, suchas an indirect luminance capture device described herein, relative tothe light source is used to facilitate capturing, storing, and trackingnear field data captured by the device. The sequence of adjusting aposition/orientation and image capture is repeated, resulting in aplurality of images, typically 2D images with location and orientationinformation for each image being stored in a near field data set. Thecollection of 2D images may be accessed by a regenerative algorithm 4610that processes the image data and location data to generate a lightmodel of the light source 4612

Lighting Distributions: Match to Desired Effect

In embodiments, users may indicate a desired lighting effect in aportion of a room or the like, such as through a real-time augmentedreality user interface. A user may want light of a color or range ofcolors with a range of intensities to illuminate a space, such as awall, ceiling, hallway, piece of art, and the like. A lighting designermay utilize lighting effect user interface to capture the user'spreferences in a structured set of values, such as light color range,light (e.g., lumens and the like) intensity, light direction, distanceand the like. In embodiments, a data structure representative of thedesired lighting effect, which may alternatively be referred to hereinfor convenience as a “bloom effect,” may be captured from the structuredset of values or indicated by a user viewing and selecting among virtualemulations of lighting effects. In embodiments, a designer or the likemay attempt to find a light source (e.g., fixture) that can produce thedesired lighting effect while meeting other criteria such as size,location, cost and the like.

In embodiments, a system that facilitates determining characteristicsand values thereof of light sources that produce the desired lightingeffect may process the structure data that is descriptive of the desiredbloom effect as noted above to generate one or more light sourcecharacteristics that are indicative of a light source that may producethe desired effect. In embodiments, machine learning may be used toimprove on matching light source characteristics with desired bloomeffects. A matching algorithm may be employed that matches the lightsource characteristics to a library of light sources that may facilitateidentifying light sources based on certain characteristics and one ormore values for those characteristics. In embodiments, a light sourcecharacteristic may be a color range, such as a warm color range, and avalue for such a range may be a subset of warm color values. Therefore,a matching system may first detect light sources that produce warmcolors. This subset may be further analyzed to match lights that producethe desired subset of warm color value. In embodiments, light sourcecharacteristics may include shape characteristics, such as shapes of orwithin the pattern of illumination and shadow produced by a lightsource, and a value may include a name for the pattern (e.g., a“butterfly” shape) or values (e.g., geometric data) that represent theshape of the pattern (e.g., a circle, a cone, a triangle, a square, etc.having one or more stated dimensions). Shape characteristics may bemeasured and stored for different distances from a light source and fordifferent surfaces upon which illumination is cast. For example, afixture may cast a butterfly shape at three feet from the light sourceon a planar surface that faces the light source, but the fixture maycast an even, or “unshaped,” illumination pattern further away, asdirectional effects from individual bulbs diminish farther away from thefixture. Additional attributes, such as cost, size, and the like may beincluded in a light source characteristic search (e.g., lights costinggreater than $X may be excluded from further assessment as candidatelight fixtures.). A user may view and select among the candidate lightsources in a user interface. In embodiments, matching a light source toa bloom effect may be based on similarity of values of the identifiedcharacteristics with values of corresponding characteristics of lightfixtures. In this way, lights that may not exactly match a specificcolor value or a specific shape of illumination may be classified ascandidate light fixtures for further review by a human, such as the userand the like. To facilitate differentiation among light sources, somecharacteristics may be weighted more heavily than others. In an example,a color output by a light may be weighted more heavily than a cost forlights so that a user may be presented with lights whose cost is outsideof a cost range characteristic. A user interface may be configured toallow a user to determine the weighting of characteristics as a degreeof compliance by the light sources with the characteristics of thedesired bloom effect.

In embodiments, a bloom effect data structure may include informationthat facilitates determining a desired effect of the bloom (e.g.,illuminating a walkway), an aesthetic filter effect of the bloom (e.g.,washing along a wall), an emotional filter effect (e.g., developing awelcoming and friendly atmosphere), and the like. This information maybe coded, such as on a scale for an emotional filter from friendly tobusiness-like.

In embodiments, a user interface of a system that facilitates matchinglight sources to a desired bloom effect may facilitate visual comparisonof the desired lighting bloom effect and a bloom effect of at least onelight source. In embodiments, the user interface may facilitatepresenting the desired lighting bloom effect and a bloom effect of atleast one light source in an environment, such as in a virtual realityrendering of a bloom effect of a light source on an image of a specificenvironment (e.g., the user's hallway). The user interface may alsofacilitate a user scrolling through a library of light fixtures byviewing their bloom effects. In embodiments, the presentation mayinclude a live view of an environment that the user interface processedthrough an augmented reality rendering system to present either thedesired lighting bloom effect or a bloom effect of a light source. Inembodiments, a user may specify a bloom effect region within anenvironment, such as a wall, floor, or other substantially planarsurfaces.

In embodiments, light source selection may be based on matching desiredbloom effects to light source bloom data in a library of light sources.Light source bloom data in a library may include characteristics thatmay be like those determined for a desired bloom effect, such asaesthetic filter effects, emotional filter effects, and the like. Abloom matching system may seek bloom effects in the library that havecomparable characteristics and values thereof. By searching forcomparable bloom effects, a set of candidate light fixtures,specifically those fixtures that produce the bloom effects that are mostsimilar to the desired bloom effect may be identified and presented tothe user. The user may be presented the desired bloom effect andcandidate bloom effects side-by-side to facilitate visual comparison.Once a candidate set of light fixtures is determined based on the bloomeffect matching, other factors such as cost, size, and the like may beused to further filter the candidate set. In embodiments, a single lightfixture may produce multiple different bloom effects, such as due to useof different light bulbs. Therefore, by searching based on bloom effectrather than fixture characteristics, fixtures that have light-bulbdependent bloom effects may be detected.

In embodiments, to facilitate, for example, searching through a libraryof light fixtures based on a desired light bloom effect, light bloomeffects for light fixtures need to be accessible. This may beaccomplished by capturing light data from many different light fixtureinstallations, including virtual installations that may be generated bya human user and/or through automated machine learning-based approaches,such as artificial intelligence and classifying the installations(including, the light fixtures, bulbs being used and the like) accordingto one or more lighting effects created by the light fixtures in theinstallations. The captured light data and the classification may bestored in a light fixture library as properties of the lighting objectused in each installation. When searching for lighting effects thatcontain certain properties, these captured light effects may be searcheddirectly for the properties. In embodiments, classifying the lightingeffects may be based on a measurable effect on a group or an individual,such as a productivity effect, a health effect and the like. Inembodiments, classification may be done by an expert system, anartificial intelligence system and the like. In embodiments, trainingfor an artificial intelligence system may be based on a training set oflighting effects created by, for example, humans classifying lightinginstallations.

In embodiments, a user's intent in selecting a light bloom effect (e.g.,establish a warm setting in a space) may be mapped to light fixturecontrols in play when the user expresses his intent. In this way, thelight fixture controls that produce the user intent may be applied toone or more light sources in other spaces. User feedback fromexperiencing these other spaces may be applied to a machine learningfunction that facilitates developing an understanding of a relationshipbetween user reactions of the controlled light sources in the otherspaces with the user's intent. The understanding may be useful inrefining the process of converting a user's intent to lighting fixturecontrols, such as by adjusting a data set that facilitates mapping auser's intent to lighting controls. Referring to FIG. 47 , a flowdiagram is depicted for generating a candidate set of light sourcesbased on attributes of a desired bloom effect being compared to lightsource features. A bloom effect description 4702, such as a data set ofbloom luminance values and the like, may be retrieved by a processorthat analyzes the data set to detect features of the bloom 4706 thatmight align with light source features 4708, such as color, spread oflight, intensity, and the like. The processor may access a library oflight sources 4704 to retrieve light features 4708 for a plurality oflights and compare them in a comparison step 4714 to the bloom featuresdetected. Values 4710 for the light features that match with thedetected bloom features may be retrieved from the library 4704 andcompared to values for the detected bloom features for the bloom 4702.Values that sufficiently match may indicate candidate light sources 4712for producing the desired bloom 4702. A set of candidate light sources4718 may be produced for use in a user interface to view a comparison ofthe desired bloom effect and bloom effects from the candidate lights.

Referring to FIG. 48 , a flow diagram is depicted for generating acandidate set of light sources based on a comparison of a desired bloomeffect to light bloom effects stored in a library of light sources. Abloom effect description 4702′, such as a data set of bloom luminancevalues and the like, may be retrieved by a processor that analyzes thedata set to detect features of the bloom 4706′, such as color, spread oflight, intensity, and the like. A library of light sources with bloomeffects 4704′ may be accessed by the processor to match bloom features4720 for light sources in the library with the detected bloom features4706′. Matches may be used to identify corresponding light sources 4722for the matching blooms. The candidate light sources 4722 may be savedas a set of candidate lights 4728 for use in a user interface to view acomparison of the desired bloom effect and bloom effects from thecandidate lights.

Near Field: Metrics

In embodiments, methods and systems for capturing illumination in a nearfield for light sources and generating data sets that represent nearfield illumination are described herein. Processing near fieldillumination data may yield metrics that may be beneficial for variouslight matching, selection, and modeling methods and systems describedherein. Generating metrics of near field illumination data may involvepattern matching, artifact detection, light quality rating, brightnessclassification, color classification, spectral characterization, and thelike.

In embodiments, metrics associated with patterns and/or artifacts may begenerated by counting occurrences thereof, determining size and or scaleof artifacts and patterns, and aggregating measures related to theartifacts and patterns, such as size of patterns or artifacts, scale ofpatterns or artifacts, occurrences of patterns or artifacts and thelike. In embodiments, pattern detection, artifact detection and the likemay be performed through processing near field data, such as images,off-axis slices through a near field volume data, and the like withimage analysis algorithms that employ feature detection techniques andthe like. A pattern may be detected through proximity detectiontechniques that may be triggered off a detected artifact, such as alocalized substantive change in intensity, and the like. Nearby datavalues in a near field volume data set may be evaluated for the presenceof and/or continuation of a similar artifact which may indicate apattern. Aggregating the measures may produce at least one of aplurality of distinct near field metrics for a light source. Inembodiments, metrics may further include a mixing distance metric, ascale of artifacts metric, a contrast in the near field metric, a lightquality metric, an output color metric, a brightness output metric, aspectral range metric and the like. A mixing distance metric may be ameasure of distance from a light source at which a magnitude ofartifacts drops below a threshold of artifact visibility.

In embodiments, patterns and artifacts may be measured cooperatively,such as by detecting an indication of size, scale and frequency ofoccurrence of artifacts in a detected light pattern produced by thelight source. A contrast metric may likewise be based on detectedpatterns and artifacts in that it may be an indication of the intensityof at least one of patterns and artifacts detectable proximal to thelight source. Such an indication of intensity may be represented as aratio of at least one of the detectable patterns and the detectableartifacts. A greater number of detectable patterns and artifacts mayindicate a greater contrast ratio metric. Whereas fewer detectablepatterns and artifacts may indicate a lower contrast ratio. Inembodiments, calculating metrics of near field light volume data mayinclude processing luminance data such as values of theta and phi for arange of positions in the light volume. In embodiments, machine learningalgorithms may play a role in calculating near field metrics by beingapplied to improve algorithms that associate candidate metrics with datavalues from a plurality of near field data sets.

Augmented Reality: Lighting Methods

In embodiments, augmented reality lighting emulation may include athree-dimensional model for an environment, where a position (andoptionally direction) for a lighting fixture can be specified, such asin a user interface of the model, such that known lightingcharacteristics of the lighting fixture can be modeled (such asillumination effects on surfaces in the model) and presented to a userin an augmented reality format that includes a representation of theenvironment (such as a photo, animation, or video) with overlays thatdisplay the illumination effects created by one or more lightingfixtures, including ones based on near field and far fieldcharacteristics, and interactions thereof (such as based on illuminationmodels that account for interference, reflection and the like). Thus,the user experiences an augmented version of the environment thatdemonstrates the effect a particular set of the lighting fixture(s)would have on or in the environment. In embodiments, augmented realitylighting emulation may include a combination of mobile devices. Inembodiments, a user may select a light fixture with an interface orapplication of the first device and position the first device in theenvironment, such as against a wall of a room where a wall sconce mightbe desired, so that the augmented reality system can understand, such asfrom positioning data obtained from the first device, where the lightingfixture is to be positioned for purposes of augmented reality renderingof the fixture. The lighting fixture itself, as well as the physicallocation and orientation of the emulated light in the room, can bedetermined, such as using the various techniques described throughoutthis disclosure, and a corresponding model of the selected lightingfixture can be used to emulate an impact of the emulated lightingfixture in the room (e.g., in the near field and far field areas of theroom relative to the lighting fixture). By detecting or retrieving atype of lighting fixture being emulated, and a location and anorientation of the device emulating the light in the environment, alighting model of the emulated light fixture may be applied in anaugmented reality setting to a portion of the environment in which thefirst mobile device is disposed. In embodiments, a second device may bean augmented reality device (e.g., AR glasses, a smartphone with ARdisplay elements placed over a visual representation of an environment(such as a photo or video of the environment), and/or an AR headset(such as a headset configured to hold a smartphone near the eyes of theuser) that a user may wear or hold in the environment. The environmentmay be captured and modeled in a space lighting modeling function usingthe position and orientation information of the emulated light fixture.A result may be a rendering of the impact of the emulated light fixtureon surfaces and objects in the augmented reality view of theenvironment. In embodiments, the second device may be a smartphone,tablet or the like. In embodiments, the second device may be locatedoutside of the environment and may receive information, such as imagesof the environment from the first device, and process the images withthe lighting model of the selected light fixture to produce a variant ofthe images impacted by the emulated light.

In embodiments, a system for augmented reality rendering of an impact ofa light fixture on an environment may include a first mobile computingdevice, such as a smartphone or the like representing a light source(e.g., light fixture). The position and orientation of the first mobiledevice in the environment may be detectable by a second computing devicein the environment. The position and orientation may be detected throughimage analysis of images of the environment that include the firstdevice. The position and orientation may be detected by the seconddevice (such as by GPS, by triangulation to local beacons or accesspoints, or the like) and communicated through a wireless or othernetwork transmission, such as cellular, Bluetooth™, WiFi, and the like,that facilitates communicating the first device information to thesecond device or another device, such as a lighting modelling server andthe like. In embodiments, the second device may capture an image of aportion of the environment based on the detected position andorientation of the first device. In embodiments, the second device maydetect that the orientation of the first device has the screen of theuser device facing a specific wall in the environment, such as toemulate the selected light illuminating the wall. The second device mayindicate to a user of the second device to capture an image of thespecific wall. In embodiments, the second device may access a set ofimages of the environment that were previously captured and, based onthe first device position and orientation, select one or more imagesfrom the set that includes the specific wall. The second device maycommunicate the captured image and optionally the first device positionand orientation information to a lighting modeling server that mayaccess a lighting model of the emulated light source and digitally modelan interaction of the light source with elements of the environmentdetected in the captured image, such as the specific wall and the like.The second device may receive the modeled interaction from the lightingmodeling server and render the modeled interaction on its electronicdisplay, which may be an augmented reality display. In embodiments, thesecond device may perform the lighting model access and modeling ofinteraction independent of access to a light modeling server. Changes inposition and/or orientation of the first device may be detected, andoptionally tracked so that the modeled interaction may be updated as thedevice is repositioned. In embodiments, a user of the first device mayprovide an indication, such as by holding the first device stably for aminimum duration of time or through voice or manual entry, that thelight emulated by the first device is ready to be modeled. Inembodiments, a user of the first device may position and optionallyorient the first device in the environment temporarily while indicating,such as through the user interface of the device, via a gesture, viavoice command and the like to use the location and orientation of thedevice for emulation. The user may then move the first device at willwithout impacting the resulting emulation and modeling of interactionswith portions of the environment.

In embodiments, at least one of the first device and the second devicemay include a user interface that facilitates access to a library oflight fixtures from which a user can select a light fixture to emulate.

In embodiments, a multi-device emulation of lighting interactions withan environment may include two devices interacting with the environmentand each other. A first of the two devices may be disposed in theenvironment with an image of a selected light fixture rendered on itsuser interface display. Position and optionally orientation informationabout the first device relative to features in the environment may beavailable, such as by the first device using its position andorientation sensors to determine where in the environment it is located.The first device may communicate, such as over a wireless network itsposition information, orientation information, or both so that at leastone of the other devices participating in the multi-device emulation mayuse the first device location information for modeling interactions ofthe emulated light with the environment. In embodiments, the seconddevice participating in the multi-device emulation may render in itsuser interface an illumination effect of the selected light fixture on atarget portion of the environment in response to a combination of amodel of luminance of the selected light fixture, at least one ofsurfaces and objects in the target portion of the environment, and thelocation and orientation information of the first device. The seconddevice may be an augmented reality processing device that may render anillumination effect of the selected light fixture based at least in parton a position and orientation of the second device in the environment.The target position may be captured, such as by using a camera featureof the second device. In embodiments, changes in location and/ororientation of the first device may result in near real time changes ininteraction modeling and rendering on the second device.

In embodiments, the luminance model of the emulated light fixture mayincorporate at least one of near-field luminance characterization of thelight fixture and far-field luminance characterization.

In embodiments, a multi-device emulation of lighting interactions mayinclude the first device, the second device and a lighting spacemodeling server that generates a data set that describes theillumination effect of the selected light fixture on the portion of theenvironment that the second device uses for rendering.

In embodiments, augmented reality techniques may be used in anembodiment of lighting design. A method for such use may includeprocessing an augmented reality image to detect light sources, such asactual and/or emulated light sources. The method may also includeprocessing the augmented reality image to detect surfaces (e.g., walls,windows, ceilings, floors, and the like) and/or objects (e.g.,furniture, vehicles, artwork, plantings, staircases, and the like). Auser, such as a lighting designer may be facilitated by a lightingdesign system in disposing of at least one virtual light source in theaugmented reality image. Further processing of the updated augmentedreality image may include processing near-field luminance data,far-field luminance data, and the like of the disposed virtual lightsource with a lighting space model. In response to the processing, theaugmented reality image may be updated to depict illumination ofportions of the augmented reality image in response to the lightingspace model. If the detected surfaces and/or objects are present in theportion of the environment impacted by the illumination, interactionsbetween the illumination model and the detected surfaces and/or objectsmay be rendered.

Referring to FIG. 49 , embodiments of the multi-device virtual/augmentedreality light modeling methods and systems 4900 are depicted. A user mayhold an emulation device 4902 that represents a selected light source4910 in a specific position and orientation in an environment. The spacemay include surfaces and objects 4904 that may be impacted in a modelingof lighting of the selected light source. A second device 4908 may beused in the multi-device virtual/augmented reality modeling approach torender an effect of the selected light 4910 based on a position and/ororientation of the emulation device 4902 that may be a smartphone or thelike. The exploded view of device 4908 depicts a rendering of a modelingof an impact on the environment of the selected light source based on amodel of the selected light source 4910′. In this variation, object4904′ may be illuminated on a top surface by the light 4910′ and maycause shadowing of a back surface of the object 4904′ and a region ofshadow 4912 caused by the object 4904′ based on the position andorientation of the selected light source 4910.

K: Color: Skyglow

In embodiments, design of lighting in an environment may be configuredto emulate natural light effects, such as sunrise, sunset, and the like.Additionally, lighting design may emulate other natural light effects,such as sunlight impacting a window or skylight and the like.Accomplishing specific effects, such as these natural effects mayinvolve controlling one or more lights in an environment, optionally incoordination. To emulate sky color, for example, on a ceiling of a roommay require coordinating control of lights that are directed at least inpart at the ceiling (e.g., uplights) and other lights that are directedat least in part at walls, floors, or the like (e.g., downlights) in thespace. To emulate a physical effect of light impacting a window, as aperson inside the space may perceive it, light directed at a wall or ata ceiling for a skylight may be controlled to produce an effect similarto external sunlight impacting the window or skylight. Control of lightsthat coordinate with a time of day (e.g., the path of the sun throughthe sky), a weather forecast (e.g., clouds, full sunshine, partialsunshine, and the like), and the like may further enhance emulating anatural effect of exterior sunlight.

In embodiments, a method of emulating exterior sunlight effects in aspace may include controlling a plurality of lights, such as first lightand a second light for illuminating the space. The first controlledlight may be disposed to illuminate a first region of the environment,such as to mimic sky color. Factors that may impact control of the lightmay include a user input, such as a preferred effect, a time of day,such as to emulate how the sunlight may impact the space at thespecified time of day, and the like. The second controlled light maycoordinate with the first light to maintain a preferred level ofillumination throughout the space. In embodiments, while the first lightmay illuminate a ceiling of the space with a mid-day sun color, thesecond light may illuminate portions of the space to enhance shadowingand the like that may be caused by sunlight shining through a skylightand the like. In embodiments, the second light may be controlled tomimic a window, such as on a vertical wall in the space. The control ofaspects such as color and intensity may be coordinated between the twolights so that the overall effect of illumination in the space isconsistent. In embodiments, a sky color on the ceiling may becoordinated with a sky color of a window on a wall in the environment,and the like. In embodiments, a portion of the ceiling, such as may berepresentative of a skylight, or even a plurality of skylights on theceiling may be the target illumination space of the first light.Emulating skylight effects, such as color and the like may automaticallyadjust throughout the day to emulate how natural sunlight might look onthe target surface region. In embodiments, in addition to adapting lightcolor and the like based on time of day, lighting color may becontrolled to emulate moonlight (e.g., sunlight reflected off the moon)based on a position of the moon and time of day/night. In embodiments,specific objectives of emulating skylight may include producing effectsin the space, such as a melanopic effect with a flux ratio of at least10:1 for a portion of the environment, or a circadian action and thelike. Other specific objectives may include generating cove lightingthat emulates natural skylight, or graze lighting that complimentsoverhead skylight emulation and the like.

In embodiments, coordinated control of uplights and downlights mayfacilitate emulating skylight in a space for a specific purpose, such asperforming certain tasks and the like. While an uplight may becontrolled to emulate a skylight or the color and intensity of the skyin general, a second light may be controlled to provide light that issuitable for a specific purpose, such as office activity (e.g., aworkspace), manufacturing activity and the like. Other desired effects,such as emulating a skylight, window, lunar illumination, time-of-daycoordinated illumination, cove lighting, graze lighting and the like asdescribed above and elsewhere herein may be achieved using controlmethods and systems also described herein while meeting a lighting planfor the space for the specific purpose.

In embodiments, controlling illumination of a space to emulate skylight,such as color, direction, and intensity may include controlling adownlight to mimic sky color for a time of day, such as sunrise,mid-day, and sunset timing. This may be coordinated with controlling anuplight in response to the downlight control so that illumination in thespace produces, for example, a melanopic flux ratio of at least 10:1 ina portion of the environment, or a circadian action and the like.Depending on the type of controls available for the lights, controllingmay include adjusting one, two, or more channels of a multi-channellight. Light control channels may include color, intensity, and thelike.

In embodiments, control of light in space may, when coordinated withskylight lighting effects generation may facilitate shifting a bias oflight in the space toward a side of the space, a central portion, andthe like. Shifting the bias of light in the space may be coordinatedwith, for example, a position of the sun in the sky throughout the day,so that as the sun would naturally cause shadows to move in response tothe sun traveling through the sky, the lighting in the space would beadjusted to be directed progressively less from the East throughout thefirst half of the daylight and progressively more from the Westthroughout the second half of daylight. Shifting light bias in a spacecoordinated with the position of the sun may emulate the movement of thesun, even on cloudy days when the sun may not be distinctly visible.

Main: Data Integration with Non-Traditional Sources

In embodiments, lighting design and control may benefit from the use ofdata sources that are not directly related to lighting, such as sourcesthat indicate activity of an individual, use patterns in a space,collaborative filtered data, and the like. Such information may begathered in a range of ways including, without limitation, directly fromusers, from their electronic calendars, their social media feeds (e.g.,SPOTIFY, PANDORA, NETFLIX, YOUTUBE, and the like), social media feeds ofothers in which a user is tagged, wearable sensors that may producebiomarkers and the like about a user, activity monitors, motion trackingin an environment, data gathered from groups of similar users, and thelike.

In embodiments, time zone information about a user, such as recent timezone changes and/or upcoming time zone changes may be useful in controlof lights in the user's environment to, for example, assist the user'snatural adjustment of his body clock to the new time zone. When upcomingtime zone changes are detected, such as by a user's travel plans thatmay be recorded in the user's calendar and the like, lighting controlmay optionally be adjusted to assist the user getting ready for the newtime zone, such as adjusting timing of lighting in the user'senvironment over a transition period to emulate the new time zone. Inembodiments, when a user finds himself in a new time zone with differentnatural light timing, a lighting control system may adjust lighting inthe user's environment, such as color and the like to facilitateadjusting to the new time zone.

In embodiments, light control may be centralized for an environment ordistributed to at least a portion of the lights in the environment, suchas for smart lights with integrated controllers and the like.Interfaces, such as communication networks and the like between externaldata sources, such as wearable sensors, and light control, whether it iscentralized or distributed may facilitate control of lights in responseto the interpretation of data from the wearable sensors. Interpretationof wearable sensors and the like may include detection of fatigue,anxiety, and the like that may be input into an adaptive lightingcontrol algorithm that can adjust light color, intensity, direction, andthe like to facilitate mitigating effects such as fatigue, anxiety andthe like.

In embodiments, lighting design control may benefit from access toinformation about users, groups, their likes, preferences, habits, priorlighting environments (e.g., at a former employer or at home) and thelike. Aligning, for example lighting preferences for a target group ofusers with a similar group of users for which lighting preferences arenot directly accessible may enable predicting lighting preferences forthe target group of users. In embodiments, predictions regardinglighting preferences and the like may be improved through use offeedback from users and machine learning applied thereto. Inembodiments, gathering feedback from individual users, groups of users,such as customers, experts and the like may contribute to improvementsin lighting design guidance that may be made available to a lightingdesigner in an intelligent lighting plan and design system.

In embodiments, lighting design may further be enhanced throughintegration of data sources, such as architectural plans, includingmaterial choices, line of sight aspects, building orientation, impactson natural light from nearby buildings and the like may be combined withlight source specific information such as near field characterizationdata and the like to develop a multi-dimensional understanding offactors that impact a lighting design plan for a space.

3D Structures: On-the-Fly Building of Architectural Elements forLighting Simulation

In embodiments, simulating lighting and lighting effects in anenvironment may include processing light source models to generateillumination in the environment and may include processing models ofelements in the environment, such as walls, objects (e.g., furniture,appliances, windows, and the like) to determine, among other things, animpact of the generated illumination on elements in the environment,such a surfaces that may be illuminated, others that may be shadowed,yet others that may reflect illumination, such as shiny surfaces and thelike. A lighting simulation model of an environment may include one ormore lighting effect impacting models of elements in the environment.When simulating lighting and lighting effects of a live environment,lighting effect impacting models of elements in the live environment mayneed to be accessed. While some may exist, such as in a library oflighting models and the like, others may need to be generated. Inembodiments, generating lighting models for simulating lighting effectsin an environment may include building models or architectural elementsand the like from images of an environment, such as may be generatedthrough live capture of the environment such as through an augmentedreality system.

In embodiments, configuring a three-dimensional space for lightingsimulation may involve capturing information descriptive of physicalaspects of an environment (e.g., walls, objects, such as tables, chairs,desks, plants, household objects, vehicles, and the like). Inembodiments, the captured information may be stored as athree-dimensional point-cloud representation of elements in theenvironment. To transform the descriptive information into architecturalelements for which lighting effect impacting models may be referencedduring simulation, machine learning may be applied to the descriptiveinformation to facilitate identifying the elements and therefore theircorresponding models. In embodiments, machine learning may, for example,help distinguish a free-standing object from an object that is placedagainst a wall in the environment, and the like. In embodiments, lightmodeling aspects of the detected architectural elements and features maybe determined based on the machine learning, such as by matching a modelin a library of element lighting models based on similarity of thethree-dimensional information in the point-cloud that represents anelement. Alternatively, a model for an object may be generatedon-the-fly based on detected properties of the three-dimensional object,such as may be determined from image analysis and the like. Lightmodeling aspects of the detected architectural features may includereflectance by the feature of light coming from a known angle relativeto the architectural feature. Light modeling aspects of the detectedarchitectural features may include a surface type for at least onesurface of the feature, such as reflective, and the like. Inembodiments, light modeling aspects of the detected features may bestored in a library of elements suitable for use in a light space model.A lighting space model for the three-dimensional space for lightingsimulation may be configured by incorporating the detected architecturalfeatures/elements and their corresponding light modeling aspects. Inembodiments, a lighting space model for the three-dimensional space forlighting simulation may be configured by referencing the library ofarchitectural features and incorporating corresponding light models forarchitectural features referenced in the library.

In embodiments, information descriptive of the physical environment maybe captured by using a digital camera, a three-dimensional sensor, acamera-equipped personal computing device, and the like. Capturing andprocessing the three-dimensional descriptive information may includegenerating measurements of elements in the environment and distancesbetween the elements. In embodiments, the use of machine learning mayinclude processing point clouds of the environment through a machinelearning process. In embodiments, it may be desirable to include a floorplan of the environment; therefore, generating a floor plan or areflected ceiling plan of the environment may be included whenconfiguring a three-dimensional space for lighting simulation.

In embodiments, other user interface technology may be used includingwithout limitation, artificial reality, and the like.

In embodiments, configuring a lighting space model may include detectingat least one light source in the environment, and incorporating lightmodeling aspects of the light source, such as a light source model andthe like.

In embodiments, configuring a three-dimensional space for lightingsimulation may involve capturing visual information representative of aphysical environment (e.g., walls, objects, such as tables, chairs,desks, plants, household objects, vehicles, and the like). Inembodiments, the captured information may be stored as athree-dimensional visual representation of elements in the environment.To transform the descriptive information into architectural elements forwhich lighting effect impacting models may be referenced duringsimulation, machine learning may be applied to the descriptiveinformation to facilitate detecting either surfaces, edges betweensurfaces, or a combination thereof. In embodiments, machine learningmay, for example, help distinguish a free-standing object from an objectthat is placed against a wall in the environment, and the like. Inembodiments, light modeling aspects of the detected architecturalelements and features may be determined based on the machine learning,such as by matching a model in a library of element lighting modelsbased on similarity of the three-dimensional information in thepoint-cloud that represents an element. In embodiments, machine learningmay include applying machine learning to an output of the analyzing animpact of illumination on at least one of the surfaces and the edges toimprove generating the reflective model. Alternatively, a model for anobject may be generated on-the-fly based on detected properties of thethree-dimensional object, such as may be determined from image analysisand the like. Light modeling aspects of the detected surfaces and/oredges may include reflectance by the surface and/or edge of light comingfrom a known angle relative to the surface. Light modeling aspects ofthe detected architectural features may include a surface type for atleast one surface of the feature, such as reflective, and the like.Physical relationships among the detected surfaces and edges, such aswhich combinations of surfaces and edges make up an architectural objectmay include information that facilitates determining a relativeorientation of a plurality of the detected surfaces. An exemplaryapproach to generating a reflective model of the detected surfaces andedges may include analyzing an impact of illumination on at least aportion of the detected surfaces and edges. A lighting space model forthe three-dimensional space for lighting simulation may be configured byincorporating the detected surfaces, edges, their relative orientationand a corresponding reflective model for the incorporated surfaces andedges.

In embodiments, visual information representative of the physicalenvironment may include one or more images that may be captured by usinga digital camera, a three-dimensional sensor, a camera-equipped personalcomputing device, and the like. Capturing and processing thethree-dimensional visual information may include generating measurementsof elements in the environment and distances between the elements. Anelement in the environment may include at least one surface and at leastone edge between surfaces. In embodiments, the use of machine learningmay include processing visual information of the environment through amachine learning process. In embodiments, it may be desirable to includea floor plan of the environment; therefore, generating a floor plan or areflected ceiling plan of the environment may be included whenconfiguring a three-dimensional space for lighting simulation.

In embodiments, other user interface technology may be used includingwithout limitation, artificial reality, and the like.

In embodiments, configuring a lighting space model may include detectingat least one light source in the environment, and incorporating lightmodeling aspects of the light source, such as a light source model andthe like.

In embodiments, a reflective model of surfaces and/or edges in theenvironment may include reflectance by the surface and/or edge of lightdirected at a first angle relative to the surface and/or edge.

In embodiments, when generating a light impacting and/or reflectancemodel of an object detected in an environment via machine learning andthe like, features of the object, such as a surface type and the likemay be used to identify one or more candidate models in a library ofobject lighting models may be used to facilitate generating the newobject model for use in simulating effects of lighting in theenvironment. A model of an object in the library that substantiallymatches surface type with an object in the environment, aspects of thelibrary object model may be automatically adapted to comply with theobject in the environment, such as any of the dimensions of the object,the orientation of the object surfaces as defined by the model, and thelike. A newly generated lighting/reflectance model may be classified andstored in the library to facilitate its use in other environments, andthe like.

Rendering Pipeline: Low Res on the Device and High Res Streamed ThroughCloud

In embodiments, lighting simulation of an environment, such as renderingan impact of a near field illumination of a light source on objects inan environment may be rendered for a user to view how a selected lightsource may appear and may illuminate an environment such as the user'shome and the like. Rendering may be based on a three-dimensional and thelike models of the light source, its near field illumination, objectsand features of the environment and the like. To facilitate efficientuse of computing and network resources, and the like, rendering may bedifferent for different target viewing devices. Additionally, renderingone or more images for display at different resolutions may be performedon different computing devices, so that, for example, high resolutionrendering may be performed by a computing server that may have greatercomputing power for such rendering than a mobile device. Therefore, apipeline of rendering may be configured and may include at least onerendering server and at least one mobile device. Depending on the amountof data to be rendered, effectively the resolution of the image beingrendered, one of the computing devices in the rendering pipeline may beselected. In embodiments, rendering of a portion of an environment maybegin on a mobile device to facilitate providing a quick look at thesimulated lighting environment. The resolution of the rendered portionof the environment may be increased while the portion of the environmentremains on the display of the device. When the resolution of the portionreaches a server-based rendering minimum threshold, rendering may bemoved along the pipeline from the mobile device to the server.Determination of when a server is used for rendering and when anotherdevice in the pipeline is used for rendering may be based on aspectssuch as processing bandwidth of devices in the rendering pipeline,availability of high resolution rendering data, time since a portion ofan environment has been identified for rendering, and the like.

In embodiments, security of data in the rendering pipeline may furtherbe enabled through features such as key-based compression, encoding,blockchain and the like.

In embodiments, a rendering pipeline may facilitate rendering lightingeffects based on a near field light source illumination model. Renderingalong a rendering pipeline for near field lighting effects may includerendering a first image, such as a low resolution image to provide aquick, initial view of the effect of a light placed in an environment(e.g., such as in an augmented and/or virtual reality display and thelike).

In embodiments, rendering may include simulating lighting effects withina virtual environment. In embodiments, rendering may include convertingdata produced by simulating lighting effects within an environment intodisplayable data. Data produced by simulating lighting effects may beprocessed by a rendering feature to produce a range of resolutionimages, such as low resolution images (e.g., for rapid rendering onlower power computing devices) and high resolution images (e.g., forhigh fidelity display), and the like. In embodiments, a low resolutionrendered image may be suitable for use in a rapidly changingenvironment, such as a video game experience. A high resolution renderedimage may be suitable for use when a more photorealistic effect isdesired. A rendering pipeline may receive an indication of the type ofuse of lighting effect simulation output data and determine a devicealong the pipeline that is best suited for rendering an image that iscompatible with the type of use indicated.

In embodiments, a multi-device rendering pipeline may be implemented bya first device in the pipeline receiving, such as from a server, ablockchain-secured digital image that may include content representativeof a low-resolution simulated lighting image of an environment; andrendering the low-resolution image with the first device, which may be amobile device, such as a smartphone and the like. The rendering mayinclude processing the received content to generate a visualization ofan impact of a light disposed at a first location and oriented in afirst orientation in the environment on elements in the environment.Rendering may be moved along the rendering pipeline to a differentdevice in the pipeline based on an indication received through a userinterface of the first device of a portion of the environment (e.g., asubset of the rendered low-resolution image, and the like) to render inhigh resolution. In embodiments, the resolution to render is greaterthan a server-rendering threshold. In embodiments, communication betweenand among devices in the rendering pipeline may be secured through ablockchain-based security protocol. In embodiments, the first device maybe a smartphone. In embodiments, the different device may be a networkedserver and the like. In embodiments, the high resolution image renderedon the server device in the rendering pipeline may be received as ablockchain-secured message delivered in the pipeline and displayed by amobile device, such as a smartphone, tablet and the like. Inembodiments, the digital image content representative of the subset mayinclude a full geometric model of the indicated subset. In embodiments,rendering may be based on a full geometric model of at least one of theenvironment, a light source, an object in the environment, and the like.In embodiments, rendering may be performed by a device type in thepipeline such as a virtual reality capable device, an augmented realitycapable device and the like. In embodiments, digital image content mayinclude at least one of a near field illumination model of illuminationproduced by a light source and a far field illumination model ofillumination produced by the light source. In embodiments, theblockchain may secure a low resolution image of a portion of theenvironment rendered on a mobile device in the rendering pipeline and ahigh resolution image of the portion of the environment rendered on acomputing server device in the pipeline.

In embodiments, a rendering pipeline for rendering lighting effects of alight source may rely on security features, such as blockchain and thelike. A method of incrementally increasing resolution rendering of aneffect of lighting may start by receiving a first blockchain-securedmessage that may include digital image content representative of anoutput of simulation of lighting effect on an environment and rendering,via processing the content of the message, a first resolution version ofan impact on elements in the environment, such as of a light disposed ata first location and oriented in a first orientation in the environment.Subsequent blockchain-secured messages of digital image content of thesimulated lighting effect (e.g., additional content that may enableincreasing the rendering resolution) may be received and combined withthe most recently rendered image to produce an incrementally increasedresolution image. These method steps may be repeated to incrementallyincrease resolution. In embodiments, the first resolution version may berendered by a mobile device. As the effective resolution of combiningthe previously rendered image and the subsequent content increases, whenthe effective resolution exceeds a resolution for which the mobiledevice can properly render the image, subsequent rendering activity maybe performed by a second device in the rendering pipeline, such as anetworked server and the resulting image may be streamed to the mobiledevice for display. In embodiments, the digital image content in any ofthe blockchain-secured messages may include a full geometric model. Inembodiments, all digital image content may be derived from a fullgeometric model of the environment.

In embodiments, a rendering pipeline may also be applied to rendering anear-field illumination effect as the near-field effect is captured byan indirect near field illumination capture device. Such a device may bemoved incrementally in three dimensions relative to a light source tocapture several multi-dimensional images of the near field. As eachmulti-dimensional image is captured, an increased resolution image ofthe near field illumination effect may be rendered. Based at least inpart on the resolution resulting from combining all priormulti-dimensional images, different computing devices within a renderingpipeline may be used for the rendering.

In embodiments, rendering a near field illumination effect with arendering pipeline of computing devices may include receiving at a firstcomputing device a first multi-dimensional image of illuminationproduced by a light source and captured with an indirect near fieldillumination multi-dimensional image capture device and rendering afirst resolution representation of the near field illumination (e.g.,for display on a user interface of the first computing device).Additional position differentiated multi-dimensional images ofillumination from the light source captured by the indirect near fieldcapture device may be received and combined into increasingly higherresolution renderings of the near field illumination. The renderingpipeline may manage data flow to the rendering device to provide onlythe amount of information (e.g., a maximum count ofposition-differentiated multi-dimensional images) that the renderingdevice can effectively render; in other words when the resultingresolution of sending an additional position-differentiatedmulti-dimensional image would exceed a rendering threshold for thedevice, further multi-dimensional images may not be delivered up therendering pipeline to the device. Rather, a different device in thepipeline may be activated to render subsequent images with resolutionthat exceeds the rendering device rendering threshold. In embodiments,blockchain security may be applied to this near field rendering process.In embodiments, each of the first and subsequent multi-dimensionalimages may be blockchain-secured.

Color: Programmable “Modes”

In embodiments, control of a multi-channel programmable light source mayenable achieving a consistent CIE map location (e.g., a consistent “x”and “y” in the map) while adjusting spectral content, such as byadjusting individual channel intensities. Therefore, there may beseveral ways to control a multi-channel programmable light to achieve agiven effect. Various modes that may be achieved include color qualitymode, (e.g., maximizing CRI or TM-30), efficacy mode, which may includemaximizing lumens/watt, circadian model that may focus on maximizingequivalent melanopic lux (EML), color bias mode that may includeoversaturating a single color (e.g., red, green, or blue) as a spectralcomponent of an “x” and “y” mapped point on the CIE color map, restmodel that may include minimizing blue/EML content, and the like.

In embodiments, maintaining a consistent CIE point for a light sourcewhile changing the focus at that point may include adjusting a singlechannel intensity, such as red to facilitate improving the red colorresponse of cameras and the like capturing images of objects illuminatedby the light source.

In embodiments, light sources with four or more light producing elementsor channels (e.g., individual Light Emitting Diodes (LEDs)) can generatea single output color in nearly an infinite number of ways. This may beenabled by combining different amounts of the light output from theindividual LEDs/channels. While a resulting color remains consistentbetween these combinations, the spectral power distribution of thegenerated light can be significantly different. This may result insignificant variation for the various features of the output light suchas color rendering, health impacts, and power efficiency. Therefore, inLED light sources with four or more channels, several color modes can bedefined to give the user the flexibility of choosing their feature ofinterest. In embodiments, select color modes may be designed andimplemented in several stages. At a first stage, the spectral powerdistribution and electrical power characteristics of the individual LEDsmay be measured. This stage may be carried out in an optics laboratoryusing a measurements device called an integrating sphere. Next, the dataacquired in the first stage is used to formulate and solve anoptimization problem to obtain the individual LED channel intensitiesfor each of the target colors and each of the color modes. Inembodiments, mathematical models that define lighting effects andfeatures of a color mode may be used in this second stage to calculatecorresponding LED channel intensities in each case (e.g., for each colormode). In embodiments, the intensity information of the light source maybe downloaded for simulation, such as in the form of a table, and thelike. The luminaire firmware may be developed such that at each point intime, the LED channel intensities are determined based on the user'schoice of the target color and color mode. In embodiments, computationaldensity in microcontrollers may allow for these calculations to be donein real-time at the fixture level rather than requiring intensityinformation tables to be used.

In embodiments, color control, such as with a multi-channel programmablelight source may include generating programmable color tuning curves,for example, to facilitate producing consistent color across a range ofchannel intensities and the like. Producing a color tuning curve mayinclude controlling four color channels, such as a pair of secondarycontrol channels that the CIE point by one of the pairs controlling the“x” index and the other of the pair controlling the “y” index. Inembodiments, the other two color channels that may be controlled mayinclude a primary control input that maps to a combination of “x” and“y” and a third control channel that facilitates controlling a dim valuefor the intensity of at least one of the channels of the light source,such as one of a plurality of LEDs in the light source, and the like. Inembodiments, the color tuning curve may be deployed (e.g., impactlighting rendered) in an augmented reality lighting simulationenvironment. In embodiments, a lighting effect in a three-dimensionalspace produced by the use of the color tuning curve may be rendered in asimulation thereof. Simulation of a lighting effect produced fromcontrolling a model of a light source based on the produced color tuningcurve may include accounting for effects relating to physicalcharacteristics of the light source (e.g., bulb type, bulb count, bulborientation, shades, filters, and the like). In embodiments, thesimulation may include rendering distance-based light source intensityfor a range of color tuning curves and the like. Renderingdistance-based light source intensity may also include handling lightsource intensity falloff over the distance from the light source for aset of ray-traces. In embodiments, multi-channel light control may alsobe achieved through a user interface that facilitates a user selecting alighting fixture to control to produce the color tuning curve. Inembodiments, the user interface may facilitate a user selecting among aplurality of color tuning factors including a programmable dimmingcurve, programmable color tuning curve, a tuning curve start point, atuning curve end point, a tuning curve dimming path that may beresponsive to a level of dimming, a color tuning path that may beresponsive to a level of dimming, and the like. In embodiments,producing the color tuning curve may be responsive to a user selectionof a tuning curve start point, tuning curve end point and at least onetuning curve waypoint between the start and end points.

In embodiments, producing a consistent color across a plurality of colormodes may be performed by a system that includes a multi-channel lightemitting diode (LED) illumination source, such as a four channel LEDlight fixture and the like. Each of the four channels may beindependently controllable for at least an amount of light output by anLED in the illumination source that corresponds to a channel. Inembodiments, the system may also include a set of mathematical modelsthat define features of each of a plurality of the color modes that,when processed with a map of LED illumination source channel controlvalues for a plurality of target illumination colors by a processor,produces a set of intensity information for each of the plurality oftarget illumination colors. In embodiments, the system may include acomputing architecture that receives an indication of a target color anda color mode and controls the four channels of the illumination sourceto produce the target color based on the set of intensity informationand the indicated color mode. In embodiments, a target color produced ina power efficiency color mode may be substantially indistinguishablefrom the same target color produced in a full power color mode. Inembodiments, the system may produce a consistent target color for arange of color modes including, without limitation, a color qualitymode, an efficacy mode, a circadian mode, a color bias mode, a restmode, and the like. In embodiments, these modes may include a colorquality mode, (e.g., maximizing CRI or TM-30), an efficacy mode, whichmay include maximizing lumens/watt, a circadian model that may focus onmaximizing equivalent melanopic lux (EML), a color bias mode that mayinclude oversaturating a single color (e.g., red, green, or blue) as aspectral component of an “x” and “y” mapped point on the CIE color map,a rest model that may include minimizing blue/EML content, and the like.In embodiments, producing a circadian mode may be achieved independentlyof third-party metrics, such as through the use of a daylight similaritymetric, and the like. In embodiments, the circadian mode may be achievedbased on circadian metrics, such as Circadian Stimulus and the like.

Near Field: Model-Based Rendering

In embodiments, lighting effect rendering may include model-basedrendering of near field effects of a light source. Model-based renderingmay include modeling of light source emissions as a set ofdirection-specific light ray-traces. Data at a plurality of positions ofthe modeled light emissions along a portion of the ray-traces may beextracted and stored in a computer accessible memory. The stored datamay be configured as three-dimensional light volume data, and the like.The light volume data may describe a three dimensional space; howevernot all positions in the three-dimensional space may be included in thelight volume data; therefore, some points in the three-dimensional spacemay be interpolated, such as based on nearest neighbor techniques.Rendering may further include determining interactions among theray-traces, such as due to the ray traces overlapping in thethree-dimensional space, and the like. Lighting effect model-basedrendering may also include a step of presenting the modeled data to auser, such as by rendering in an electronic display near field effectsof the light source, the effects may be derived from a lighting spacemodel that incorporates the light volume-data, the interpolatedplurality of points and the interactions among the ray-traces,essentially a superset of data associated with model based rendering asdescribed herein this embodiment.

In embodiments, the lighting space model may enable determining animpact of illumination from the light source by taking into accountelement lighting-related elements such as, light transparency,absorption, and reflection. In embodiments, near field model-basedrendering may work with a virtual reality controller, an augmentedreality controller, and conventional two-dimensional devices, such as asmart phone, tablet, and the like. Near field rendering may includerendering near field lighting artifacts throughout a three-dimensionalspace, such as a room in which a user is attempting to determine whichtype and location of lighting may produce a desired lighting effect.Such rendering may also consider physical characteristics of a lightsource, such as type, location, orientation, and count of individualbulbs (e.g., individual light sources). In embodiments, each individualbulb or lighting element may be associated with a corresponding set ofray traces and rendering may include rendering effects from each of theplurality of distinct light elements (e.g., individual LEDs, bulbs,lenses, and the like). Use of three-dimensional light volume data mayfacilitate detecting a shape of a lighting effect, properties of thelighting effect, such as a shape at a specified distance from the lightsource, and the like. In embodiments, such a shape may be substantiallycontinuous or discontinuous. Light volume data may include light color,reflections of the light from objects in the embodiments, and the like.

In embodiments, model-based rendering of near field illumination effectsfrom a light source may include capturing a set of data representing,for example, a three-dimensional space proximal to a light source (e.g.,a near field of the light source), the data set may include datarepresenting illuminance values of light at each of a plurality oflocations in the three-dimensional space, such as may be generated by anindirect near field illumination collection system. In embodiments, aportion of the set of data, such as a three-dimensional portion thatreferences a volume of space proximal to the light source, may beextracted and used to generate a geometric model of the portion thatfacilitates modelling an impact of the illuminance of the light sourceon objects disposed in the space proximal to the light source. Forportions of the three-dimensional space that is not fully characterizedby light values, a plurality of additional illuminances value may beinterpolated and used for model-based rendering. In addition toindirectly captured illumination from the light source, thethree-dimensional data set may include reflections from surfaces in thespace; thereby facilitating rendering bot a light field and an impact ofthe light field on objects in an environment.

Augmented Reality: Floor Planner

In embodiments, planning lighting deployments may benefit from lightingsimulation and interactive technologies such as augmented reality, andthe like. Developing a floor plan in an augmented reality lightingdesign system may further help with rendering lighting simulations. Inembodiments, planning lighting in an augmented reality environment mayinclude representing physical features of an environment as a pointcloud data structure. The point cloud data may be processed with machinelearning to generate a lighting space model of the environment, whichmay be used to produce a floor plan, a reflected ceiling plan, and thelike of the environment. In embodiments, virtual measuring techniques,such as a virtual ruler in an augmented reality display may be used whenproducing a floor plan and the like. By coupling the lighting spacemodel with an augmented reality view of the environment light sources(e.g., fixtures and the like) may be added to the lighting space modelby a user placing lighting source elements (e.g., icons and the like) inthe augmented reality view of the environment. The environment with theplaced light sources, lighting space model and the like may be renderedin the augmented reality view and may include simulated lighting effectsof the placed light sources, such as based on near field illuminationcharacterization of the placed light sources. In embodiments, renderingin the augmented reality view may include accounting for aspects such astransparency, absorption, reflection and the like of the elements in theenvironment. Rendering of a floor-plan-based lighting environment mayinclude accounting for physical effects of the elements in the floorplan as well as physical characteristics of the modeled light source,such as quantity, type, orientation, and shading of individual lightelements that make up the light source (e.g., a light fixture withmultiple bulbs, and the like). In embodiments, lighting models for lightfixtures with multiple bulbs may be configured to facilitate renderingeffects from each of the individual bulbs and the like. In embodiments,lighting effects simulated may be based on an area-source model of theplaced lights that may not account for effects from individual lightingelements of the light source. In embodiments, the lighting floor planmay be at least partially configured through a user interface thatallows a user to select light sources from a library of lights, indicatea position and orientation of the selected light sources, and optionallydefine a portion of the environment for inclusion in the floor plan. Inembodiments, rendering for a floor-plan-based lighting environment maybe performed by a volumetric renderer.

In embodiments, planning lighting in an augmented reality environmentmay include representing physical features of an environment as surfacesand edges. The surface and edge data may be processed with machinelearning to generate a lighting space model of the environment, whichmay be used to produce a floor plan, a reflected ceiling plan, and thelike of the environment. By coupling the lighting space model with anaugmented reality view of the environment light sources (e.g., fixturesand the like) may be added to the lighting space model by a user placinglighting source elements (e.g., icons and the like) in the augmentedreality view of the environment. The environment with the placed lightsources, output of the lighting space model and the like may be renderedin the augmented reality view and may include simulated lighting effectsof the placed light sources, such as based on near field illuminationcharacterization of the placed light sources. In embodiments, renderingin the augmented reality view may include accounting for aspects such astransparency, absorption, reflection and the like of the surfaces and/oredges in the environment. Rendering of a floor-plan-based lightingenvironment may include accounting for physical effects of the surfacesand edges in the floor plan as well as physical characteristics of themodeled light source, such as quantity, type, orientation, and shadingof individual light elements that make up the light source (e.g., alight fixture with multiple bulbs, and the like). In embodiments,lighting models for light fixtures with multiple bulbs may be configuredto facilitate rendering effects from each of the individual bulbs andthe like. In embodiments, lighting effects simulated may be based on anarea-source model of the placed lights that may not account for effectsfrom individual lighting elements of the light source. In embodiments,the lighting floor plan may be at least partially configured through auser interface that allows a user to select light sources from a libraryof lights, indicate a position and orientation of the selected lightsources, and optionally define a portion of the environment forinclusion in the floor plan. In embodiments, rendering for afloor-plan-based lighting environment may be performed by a volumetricrenderer.

Augmented Reality: Floor Planner+Project Level AR=Mini-Schedule

In embodiments, developing a floor plan, such as for lighting sourcesand the like may be combined with an order management system that mayfacilitate integrated planning, client assessment, and orderpreparation. In embodiments, such an integrated approach may includerepresenting physical features of an environment as a point cloud datastructure. The point cloud data may be processed with machine learningto generate a lighting space model of the environment, which may be usedto produce a floor plan, a reflected ceiling plan, and the like of theenvironment. By coupling the lighting space model with an augmentedreality view of the environment light sources (e.g., fixtures and thelike) may be added to the lighting space model by a user placinglighting source elements (e.g., icons and the like) in the augmentedreality view of the environment. The user may select a model for a lightsource from a catalog of light sources that may be presented in theaugmented reality view. The environment with the placed light sources,lighting space model and the like may be rendered in the augmentedreality view and may include simulated lighting effects of the placedlight sources, such as based on near field illumination characterizationof the placed light sources. In embodiments, rendering may includerendering near-field lighting effects in the environment of the placedlight sources based on a near-file illumination model of the lightsource. In embodiments, the lighting floor plan may be at leastpartially configured through a user interface that allows a user toselect light sources from a library of lights, indicate a position andorientation of the selected light sources, and optionally define aportion of the environment for inclusion in the floor plan. Integrationwith an order management system may include populating a data object,such as may represent an order form and the like with lighting sourceitem identification information that may be useful for ordering theplaced lights. Further integration may include automatically placing atleast one order into a supply chain of lighting sources for the at leastone placed light source. In embodiments, identification information forordering a light source may be retrieved from the catalog of lightsources. A user may indicate which of the placed light sources to orderthrough the augmented reality interface. Other objects in theenvironment may be identified and automatically ordered, such as lightswitches, floor lamps, and the like by populating an order data objectsimilarly to ordering a light source. In embodiments, a lightinginstallation plan may be configured based on the generated floor planand the position and orientation of the placed light sources. Inembodiments, rendering in the augmented reality view may includeaccounting for aspects such as transparency, absorption, reflection andthe like of the elements in the environment. Rendering of afloor-plan-based lighting environment may include accounting forphysical effects of the elements in the floor plan as well as physicalcharacteristics of the modeled light source, such as quantity, type,orientation, and shading of individual light elements that make up thelight source (e.g., a light fixture with multiple bulbs, and the like).In embodiments, lighting models for light fixtures with multiple bulbsmay be configured to facilitate rendering effects from each of theindividual bulbs and the like. In embodiments, lighting effectssimulated may be based on an area-source model of the placed lights thatmay not account for effects from individual lighting elements of thelight source. In embodiments, the lighting floor plan may be at leastpartially configured through a user interface that allows a user toselect light sources from a library of lights, indicate a position andorientation of the selected light sources, and optionally define aportion of the environment for inclusion in the floor plan. Inembodiments, rendering for a floor-plan-based lighting environment maybe performed by a volumetric renderer.

In embodiments, an integrated plan, simulate, and order lighting designapproach may include representing physical features of an environment assurfaces and edges. The surface and edge data may be processed withmachine learning to generate a lighting space model of the environment,which may be used to produce a floor plan, a reflected ceiling plan, andthe like of the environment. By coupling the lighting space model withan augmented reality view of the environment light sources (e.g.,fixtures and the like) may be added to the lighting space model by auser placing lighting source elements (e.g., icons and the like) in theaugmented reality view of the environment. The user may select a modelfor a light source from a catalog of light sources that may be presentedin the augmented reality view. The environment with the placed lightsources, lighting space model and the like may be rendered in theaugmented reality view and may include simulated lighting effects of theplaced light sources, such as based on near field illuminationcharacterization of the placed light sources. In embodiments, renderingmay include rendering near-field lighting effects in the environment ofthe placed light sources based on a near-file illumination model of thelight source. In embodiments, the lighting floor plan may be at leastpartially configured through a user interface that allows a user toselect light sources from a library of lights, indicate a position andorientation of the selected light sources, and optionally define aportion of the environment for inclusion in the floor plan. Integrationwith an order management system may include populating a data object,such as may represent an order form and the like with lighting sourceitem identification information that may be useful for ordering theplaced lights. Further integration may include automatically placing atleast one order into a supply chain of lighting sources for the at leastone placed light source. In embodiments, identification information forordering a light source may be retrieved from the catalog of lightsources. A user may indicate which of the placed light sources to orderthrough the augmented reality interface. Other objects in theenvironment may be identified and automatically ordered, such as lightswitches, floor lamps, and the like by populating an order data objectsimilarly to ordering a light source. In embodiments, a lightinginstallation plan may be configured based on the generated floor planand the position and orientation of the placed light sources. Inembodiments, rendering in the augmented reality view may includeaccounting for aspects such as transparency, absorption, reflection andthe like of the surfaces and/or edges in the environment. Rendering of afloor-plan-based lighting environment may include accounting forphysical effects of the surfaces and edges in the floor plan as well asphysical characteristics of the modeled light source, such as quantity,type, orientation, and shading of individual light elements that make upthe light source (e.g., a light fixture with multiple bulbs, and thelike). In embodiments, lighting models for light fixtures with multiplebulbs may be configured to facilitate rendering effects from each of theindividual bulbs and the like. In embodiments, lighting effectssimulated may be based on an area-source model of the placed lights thatmay not account for effects from individual lighting elements of thelight source. In embodiments, the lighting floor plan may be at leastpartially configured through a user interface that allows a user toselect light sources from a library of lights, indicate a position andorientation of the selected light sources, and optionally define aportion of the environment for inclusion in the floor plan. Inembodiments, rendering for a floor-plan-based lighting environment maybe performed by a volumetric renderer.

Augmented Reality: Control of AR Objects—UX/UI

In embodiments, augmented reality objects, such as light sources and thelike may be controlled with various virtual control features that may berepresented in a computer interface, such as an augmented realityinterface. In embodiments, configuring an interface to facilitatecontrol of modeled light sources in a computer user interface, such asan augmented reality interface may include coupling lighting spacemodels of an environment in, for example, an augmented reality view ofthe environment so that a user of the computer may place light sourcesin the environment. The environment and simulations of lighting effectsof the placed lighting sources in the environment may be rendered basedon a model of illumination of the placed light sources, such as a nearfield illumination characterization of the placed light sources. In theuser interface, a plurality of virtual lighting controls may be renderedto control illumination from at least one of the placed light sources.In embodiments, the plurality of virtual lighting controls may includeuser interface elements for controlling at least one of light intensity(e.g., dimming color and/or finish of a fixture, beam angles of afixture, light color, and light color temperature to allow a user toadjust how the environment might look under these different conditions.In embodiments, the plurality of virtual lighting controls may includeuser interface elements for controlling at least one of rotation andtilt of the placed light sources to allow a user to adjust, for examplehow a light fixture may be positioned in a room and pointed toward aportion of the room such as a wall, floor, doorway and the like.

In addition to computer user interface-based virtual controls, thelights in the simulation environment may be controlled through wearablesensors that a user, such as a user in the augmented realityenvironment, may wear to detect motion and the like of the user. Thedetected motion and the like may be converted into control sequences tocontrol simulated light sources such as to dim a light, reposition alight, change its orientation, and the like. Aspects such as intensity,color, color temperature, position, orientation, tilt, rotation and thelike may each be controlled through interpretation of the wearablesensors. Augmented reality controls may include virtual controls forselecting light sourced from a marketplace of light sources.

Virtual controls in an augmented reality environment may includecontrols such as an adjustable dial that represents a range of lightingeffects producible by a placed light source. When a user interacts withthe adjustable dial, the lighting effect (e.g., color mode and the like)may be changed in the simulation, which may result in changes in theinteraction with elements in the environment. As a user changes the dialsettings, the augmented reality display of the lighting effects on theenvironment may be updated in real time or near real time. Inembodiments, the techniques described above for an augmented realityview device may be implemented with a combination of an augmentedreality device for lighting space modeling and lighting effectinteractions with an environment and a handheld computing device, suchas a smartphone and the like for controlling lights placed in theaugmented reality environment. The controls for color, intensity,position, tilt, rotation, placement relative to elements in theenvironment, and the like may be implemented in the handheld device,that may in embodiments be operated by a user in the environment.

In embodiments, a handheld device, such as a smartphone and the like maybe used in place of wearable motion sensors to indicate control of thelight sources. Movement of the handheld device may be detected by motionsensors of the device and reported to the augmented reality environmentas inputs to virtual controls on the lights. Moving the handheld deviceto a new position in the environment may be detected as an indication ofa new position for a light source being placed in the environment by theuser. Rotating the handheld device may indicate a user's desire toincrease or decrease light output and the like. These are merelyexamples of how motion data, may be used to perform lighting controlfunctions in an augmented reality lighting simulation environment.

A method for planning lighting in an augmented reality display having amachine learning-based lighting space model of an environment in anaugmented reality lighting design interface through which user selectedlight sources are automatically added to a light source supply chainorder. A method for planning lighting in an augmented reality displayhaving a machine learning-based lighting space model of an environmentin an augmented reality lighting design interface through which userselected light sources are automatically added to a light source supplychain order and having a pattern matching system that facilitatesidentifying a light source that produces a desired bloom effect based onsimilarity of bloom effect-specific light source properties withproperties of characterized light sources. A method for planninglighting in an augmented reality display having a machine learning-basedlighting space model of an environment in an augmented reality lightingdesign interface through which user selected light sources areautomatically added to a light source supply chain order and having useof a system for determining interactions among ray-traces through lightvolume data that characterizes illumination from a light source andobjects in a three-dimensional space in which the light volume data isrendered. A method for planning lighting in an augmented reality displayhaving a machine learning-based lighting space model of an environmentin an augmented reality lighting design interface through which userselected light sources are automatically added to a light source supplychain order and having an emotional content data structure for lightingdesign that is populated with machine learning optimized factors thatcontribute to emotional effects of lighting. A method for planninglighting in an augmented reality display having a machine learning-basedlighting space model of an environment in an augmented reality lightingdesign interface through which user selected light sources areautomatically added to a light source supply chain order and havingtechniques for determining light source characteristics from a desiredlighting effect data set that are used to identify candidate lightsources for producing the desired lighting effect. A method for planninglighting in an augmented reality display having a machine learning-basedlighting space model of an environment in an augmented reality lightingdesign interface through which user selected light sources areautomatically added to a light source supply chain order and having alighting space model configured with lighting models of machinelearning-based architectural elements detected in a point cloudrepresentative of a physical environment. A method for planning lightingin an augmented reality display having a machine learning-based lightingspace model of an environment in an augmented reality lighting designinterface through which user selected light sources are automaticallyadded to a light source supply chain order and having machine learninggenerate a lighting space model of an environment from a point cloudrepresentation of the environment for use in an augmented realitylighting design interface and using the lighting space model to generatea floor plan of the space based on light sources placed by a user in theaugmented reality interface. A method for planning lighting in anaugmented reality display having a machine learning-based lighting spacemodel of an environment in an augmented reality lighting designinterface through which user selected light sources are automaticallyadded to a light source supply chain order and having a lighting spacemodel of an environment in an augmented reality interface through whicha user controls lights placed in the environment via virtual lightingcontrol features presented in the augmented reality interface. A methodfor planning lighting in an augmented reality display having a machinelearning-based lighting space model of an environment in an augmentedreality lighting design interface through which user selected lightsources are automatically added to a light source supply chain order andhaving use of an indirect illumination collection facility that isdisposable throughout a three-dimensional region proximal to a lightsource that captures a plurality of multi-dimensional light sourceillumination images. A method for planning lighting in an augmentedreality display having a machine learning-based lighting space model ofan environment in an augmented reality lighting design interface throughwhich user selected light sources are automatically added to a lightsource supply chain order and having a custom tuning profile thatcoordinates changes in color and light output of a programmable lightsource to match lighting characteristics of a legacy light fixture. Amethod for planning lighting in an augmented reality display having amachine learning-based lighting space model of an environment in anaugmented reality lighting design interface through which user selectedlight sources are automatically added to a light source supply chainorder and having an indirect illumination collection facility thatcaptures near field multi-dimensional illumination indirectly from alight source by varying distance and orientation of the collectionfacility relative to the light source throughout the near field space ofthe light source. A method for planning lighting in an augmented realitydisplay having a machine learning-based lighting space model of anenvironment in an augmented reality lighting design interface throughwhich user selected light sources are automatically added to a lightsource supply chain order and having algorithms for calculating lightquality, intensity, color range, and spectral characteristic metrics ofa light source from illumination values collected at a plurality oftheta and phi differentiated positions in the light source's near fieldspace. A method for planning lighting in an augmented reality displayhaving a machine learning-based lighting space model of an environmentin an augmented reality lighting design interface through which userselected light sources are automatically added to a light source supplychain order and having a light source emulating device positioned by auser in a three-dimensional space that is presented in an augmentedreality lighting design system that uses the position and orientation ofthe emulating device to model a lighting effect in the space. A methodfor planning lighting in an augmented reality display having a machinelearning-based lighting space model of an environment in an augmentedreality lighting design interface through which user selected lightsources are automatically added to a light source supply chain order andhaving a color map to control a multi-channel light thereby producingconsistent color across a range of color modes by adjusting a pluralityof the channels based on the color map. A method for planning lightingin an augmented reality display having a machine learning-based lightingspace model of an environment in an augmented reality lighting designinterface through which user selected light sources are automaticallyadded to a light source supply chain order and having a near fielddataset characterizing a light source, generating a geometric model fromthe data set that facilitates modeling an impact of the light source onobjects disposed in the near field. A method for planning lighting in anaugmented reality display having a machine learning-based lighting spacemodel of an environment in an augmented reality lighting designinterface through which user selected light sources are automaticallyadded to a light source supply chain order and having algorithms thatconstruct three-dimensional illumination data sets from a plurality ofdistinct two-dimensional illumination data arrays that are capturedthroughout a near field space of a light source. A method for planninglighting in an augmented reality display having a machine learning-basedlighting space model of an environment in an augmented reality lightingdesign interface through which user selected light sources areautomatically added to a light source supply chain order and having asystem for adapting light controls based on machine learning from datarepresenting user activity including user time zone travel, useractivities in an environment illuminated by a light being controlled,wearable sensor biomarker user data, and feedback from users in theenvironment. A method for planning lighting in an augmented realitydisplay having a machine learning-based lighting space model of anenvironment in an augmented reality lighting design interface throughwhich user selected light sources are automatically added to a lightsource supply chain order and having a rendering pipeline in a lightingdesign system that allocates rendering to devices in the pipeline basedon a correspondence between the device rendering capability and aresolution of image content to be rendered. A method for planninglighting in an augmented reality display having a machine learning-basedlighting space model of an environment in an augmented reality lightingdesign interface through which user selected light sources areautomatically added to a light source supply chain order and havingcoordinated control of uplights and downlights to achieve a lightingeffect in an environment that mimics a sky color for a given time of day

A system for identifying a desired lighting source having a patternmatching system that facilitates identifying a light source thatproduces a desired bloom effect based on similarity of bloomeffect-specific light source properties with properties of characterizedlight sources. A system for identifying a desired lighting source havinga pattern matching system that facilitates identifying a light sourcethat produces a desired bloom effect based on similarity of bloomeffect-specific light source properties with properties of characterizedlight sources and having use of a system for determining interactionsamong ray-traces through light volume data that characterizesillumination from a light source and objects in a three-dimensionalspace in which the light volume data is rendered. A system foridentifying a desired lighting source having a pattern matching systemthat facilitates identifying a light source that produces a desiredbloom effect based on similarity of bloom effect-specific light sourceproperties with properties of characterized light sources and having anemotional content data structure for lighting design that is populatedwith machine learning optimized factors that contribute to emotionaleffects of lighting. A system for identifying a desired lighting sourcehaving a pattern matching system that facilitates identifying a lightsource that produces a desired bloom effect based on similarity of bloomeffect-specific light source properties with properties of characterizedlight sources and having techniques for determining light sourcecharacteristics from a desired lighting effect data set that are used toidentify candidate light sources for producing the desired lightingeffect. A system for identifying a desired lighting source having apattern matching system that facilitates identifying a light source thatproduces a desired bloom effect based on similarity of bloomeffect-specific light source properties with properties of characterizedlight sources and having a lighting space model configured with lightingmodels of machine learning-based architectural elements detected in apoint cloud representative of a physical environment. A system foridentifying a desired lighting source having a pattern matching systemthat facilitates identifying a light source that produces a desiredbloom effect based on similarity of bloom effect-specific light sourceproperties with properties of characterized light sources and havingmachine learning generate a lighting space model of an environment froma point cloud representation of the environment for use in an augmentedreality lighting design interface and using the lighting space model togenerate a floor plan of the space based on light sources placed by auser in the augmented reality interface. A system for identifying adesired lighting source having a pattern matching system thatfacilitates identifying a light source that produces a desired bloomeffect based on similarity of bloom effect-specific light sourceproperties with properties of characterized light sources and having alighting space model of an environment in an augmented reality interfacethrough which a user controls lights placed in the environment viavirtual lighting control features presented in the augmented realityinterface. A system for identifying a desired lighting source having apattern matching system that facilitates identifying a light source thatproduces a desired bloom effect based on similarity of bloomeffect-specific light source properties with properties of characterizedlight sources and having use of an indirect illumination collectionfacility that is disposable throughout a three-dimensional regionproximal to a light source that captures a plurality ofmulti-dimensional light source illumination images. A system foridentifying a desired lighting source having a pattern matching systemthat facilitates identifying a light source that produces a desiredbloom effect based on similarity of bloom effect-specific light sourceproperties with properties of characterized light sources and having acustom tuning profile that coordinates changes in color and light outputof a programmable light source to match lighting characteristics of alegacy light fixture. A system for identifying a desired lighting sourcehaving a pattern matching system that facilitates identifying a lightsource that produces a desired bloom effect based on similarity of bloomeffect-specific light source properties with properties of characterizedlight sources and having an indirect illumination collection facilitythat captures near field multi-dimensional illumination indirectly froma light source by varying distance and orientation of the collectionfacility relative to the light source throughout the near field space ofthe light source. A system for identifying a desired lighting sourcehaving a pattern matching system that facilitates identifying a lightsource that produces a desired bloom effect based on similarity of bloomeffect-specific light source properties with properties of characterizedlight sources and having algorithms for calculating light quality,intensity, color range, and spectral characteristic metrics of a lightsource from illumination values collected at a plurality of theta andphi differentiated positions in the light source's near field space. Asystem for identifying a desired lighting source having a patternmatching system that facilitates identifying a light source thatproduces a desired bloom effect based on similarity of bloomeffect-specific light source properties with properties of characterizedlight sources and having a light source emulating device positioned by auser in a three-dimensional space that is presented in an augmentedreality lighting design system that uses the position and orientation ofthe emulating device to model a lighting effect in the space. A systemfor identifying a desired lighting source having a pattern matchingsystem that facilitates identifying a light source that produces adesired bloom effect based on similarity of bloom effect-specific lightsource properties with properties of characterized light sources andhaving a color map to control a multi-channel light thereby producingconsistent color across a range of color modes by adjusting a pluralityof the channels based on the color map. A system for identifying adesired lighting source having a pattern matching system thatfacilitates identifying a light source that produces a desired bloomeffect based on similarity of bloom effect-specific light sourceproperties with properties of characterized light sources and having anear field dataset characterizing a light source, generating a geometricmodel from the data set that facilitates modeling an impact of the lightsource on objects disposed in the near field. A system for identifying adesired lighting source having a pattern matching system thatfacilitates identifying a light source that produces a desired bloomeffect based on similarity of bloom effect-specific light sourceproperties with properties of characterized light sources and havingalgorithms that construct three-dimensional illumination data sets froma plurality of distinct two-dimensional illumination data arrays thatare captured throughout a near field space of a light source. A systemfor identifying a desired lighting source having a pattern matchingsystem that facilitates identifying a light source that produces adesired bloom effect based on similarity of bloom effect-specific lightsource properties with properties of characterized light sources andhaving a system for adapting light controls based on machine learningfrom data representing user activity including user time zone travel,user activities in an environment illuminated by a light beingcontrolled, wearable sensor biomarker user data, and feedback from usersin the environment. A system for identifying a desired lighting sourcehaving a pattern matching system that facilitates identifying a lightsource that produces a desired bloom effect based on similarity of bloomeffect-specific light source properties with properties of characterizedlight sources and having a rendering pipeline in a lighting designsystem that allocates rendering to devices in the pipeline based on acorrespondence between the device rendering capability and a resolutionof image content to be rendered. A system for identifying a desiredlighting source having a pattern matching system that facilitatesidentifying a light source that produces a desired bloom effect based onsimilarity of bloom effect-specific light source properties withproperties of characterized light sources and having coordinated controlof uplights and downlights to achieve a lighting effect in anenvironment that mimics a sky color for a given time of day

A method of electronic display rendering of lighting distribution in athree-dimensional space having use of a system for determininginteractions among ray-traces through light volume data thatcharacterizes illumination from a light source and objects in athree-dimensional space in which the light volume data is rendered. Amethod of electronic display rendering of lighting distribution in athree-dimensional space having use of a system for determininginteractions among ray-traces through light volume data thatcharacterizes illumination from a light source and objects in athree-dimensional space in which the light volume data is rendered andhaving an emotional content data structure for lighting design that ispopulated with machine learning optimized factors that contribute toemotional effects of lighting. A method of electronic display renderingof lighting distribution in a three-dimensional space having use of asystem for determining interactions among ray-traces through lightvolume data that characterizes illumination from a light source andobjects in a three-dimensional space in which the light volume data isrendered and having techniques for determining light sourcecharacteristics from a desired lighting effect data set that are used toidentify candidate light sources for producing the desired lightingeffect. A method of electronic display rendering of lightingdistribution in a three-dimensional space having use of a system fordetermining interactions among ray-traces through light volume data thatcharacterizes illumination from a light source and objects in athree-dimensional space in which the light volume data is rendered andhaving a lighting space model configured with lighting models of machinelearning-based architectural elements detected in a point cloudrepresentative of a physical environment. A method of electronic displayrendering of lighting distribution in a three-dimensional space havinguse of a system for determining interactions among ray-traces throughlight volume data that characterizes illumination from a light sourceand objects in a three-dimensional space in which the light volume datais rendered and having machine learning generate a lighting space modelof an environment from a point cloud representation of the environmentfor use in an augmented reality lighting design interface and using thelighting space model to generate a floor plan of the space based onlight sources placed by a user in the augmented reality interface. Amethod of electronic display rendering of lighting distribution in athree-dimensional space having use of a system for determininginteractions among ray-traces through light volume data thatcharacterizes illumination from a light source and objects in athree-dimensional space in which the light volume data is rendered andhaving a lighting space model of an environment in an augmented realityinterface through which a user controls lights placed in the environmentvia virtual lighting control features presented in the augmented realityinterface. A method of electronic display rendering of lightingdistribution in a three-dimensional space having use of a system fordetermining interactions among ray-traces through light volume data thatcharacterizes illumination from a light source and objects in athree-dimensional space in which the light volume data is rendered andhaving use of an indirect illumination collection facility that isdisposable throughout a three-dimensional region proximal to a lightsource that captures a plurality of multi-dimensional light sourceillumination images. A method of electronic display rendering oflighting distribution in a three-dimensional space having use of asystem for determining interactions among ray-traces through lightvolume data that characterizes illumination from a light source andobjects in a three-dimensional space in which the light volume data isrendered and having a custom tuning profile that coordinates changes incolor and light output of a programmable light source to match lightingcharacteristics of a legacy light fixture. A method of electronicdisplay rendering of lighting distribution in a three-dimensional spacehaving use of a system for determining interactions among ray-tracesthrough light volume data that characterizes illumination from a lightsource and objects in a three-dimensional space in which the lightvolume data is rendered and having an indirect illumination collectionfacility that captures near field multi-dimensional illuminationindirectly from a light source by varying distance and orientation ofthe collection facility relative to the light source throughout the nearfield space of the light source. A method of electronic displayrendering of lighting distribution in a three-dimensional space havinguse of a system for determining interactions among ray-traces throughlight volume data that characterizes illumination from a light sourceand objects in a three-dimensional space in which the light volume datais rendered and having algorithms for calculating light quality,intensity, color range, and spectral characteristic metrics of a lightsource from illumination values collected at a plurality of theta andphi differentiated positions in the light source's near field space. Amethod of electronic display rendering of lighting distribution in athree-dimensional space having use of a system for determininginteractions among ray-traces through light volume data thatcharacterizes illumination from a light source and objects in athree-dimensional space in which the light volume data is rendered andhaving a light source emulating device positioned by a user in athree-dimensional space that is presented in an augmented realitylighting design system that uses the position and orientation of theemulating device to model a lighting effect in the space. A method ofelectronic display rendering of lighting distribution in athree-dimensional space having use of a system for determininginteractions among ray-traces through light volume data thatcharacterizes illumination from a light source and objects in athree-dimensional space in which the light volume data is rendered andhaving a color map to control a multi-channel light thereby producingconsistent color across a range of color modes by adjusting a pluralityof the channels based on the color map. A method of electronic displayrendering of lighting distribution in a three-dimensional space havinguse of a system for determining interactions among ray-traces throughlight volume data that characterizes illumination from a light sourceand objects in a three-dimensional space in which the light volume datais rendered and having a near field dataset characterizing a lightsource, generating a geometric model from the data set that facilitatesmodeling an impact of the light source on objects disposed in the nearfield. A method of electronic display rendering of lighting distributionin a three-dimensional space having use of a system for determininginteractions among ray-traces through light volume data thatcharacterizes illumination from a light source and objects in athree-dimensional space in which the light volume data is rendered andhaving algorithms that construct three-dimensional illumination datasets from a plurality of distinct two-dimensional illumination dataarrays that are captured throughout a near field space of a lightsource. A method of electronic display rendering of lightingdistribution in a three-dimensional space having use of a system fordetermining interactions among ray-traces through light volume data thatcharacterizes illumination from a light source and objects in athree-dimensional space in which the light volume data is rendered andhaving a system for adapting light controls based on machine learningfrom data representing user activity including user time zone travel,user activities in an environment illuminated by a light beingcontrolled, wearable sensor biomarker user data, and feedback from usersin the environment. A method of electronic display rendering of lightingdistribution in a three-dimensional space having use of a system fordetermining interactions among ray-traces through light volume data thatcharacterizes illumination from a light source and objects in athree-dimensional space in which the light volume data is rendered andhaving a rendering pipeline in a lighting design system that allocatesrendering to devices in the pipeline based on a correspondence betweenthe device rendering capability and a resolution of image content to berendered. A method of electronic display rendering of lightingdistribution in a three-dimensional space having use of a system fordetermining interactions among ray-traces through light volume data thatcharacterizes illumination from a light source and objects in athree-dimensional space in which the light volume data is rendered andhaving coordinated control of uplights and downlights to achieve alighting effect in an environment that mimics a sky color for a giventime of day

A method of using emotional filters for lighting design having anemotional content data structure for lighting design that is populatedwith machine learning optimized factors that contribute to emotionaleffects of lighting. A method of using emotional filters for lightingdesign having an emotional content data structure for lighting designthat is populated with machine learning optimized factors thatcontribute to emotional effects of lighting and having techniques fordetermining light source characteristics from a desired lighting effectdata set that are used to identify candidate light sources for producingthe desired lighting effect. A method of using emotional filters forlighting design having an emotional content data structure for lightingdesign that is populated with machine learning optimized factors thatcontribute to emotional effects of lighting and having a lighting spacemodel configured with lighting models of machine learning-basedarchitectural elements detected in a point cloud representative of aphysical environment. A method of using emotional filters for lightingdesign having an emotional content data structure for lighting designthat is populated with machine learning optimized factors thatcontribute to emotional effects of lighting and having machine learninggenerate a lighting space model of an environment from a point cloudrepresentation of the environment for use in an augmented realitylighting design interface and using the lighting space model to generatea floor plan of the space based on light sources placed by a user in theaugmented reality interface. A method of using emotional filters forlighting design having an emotional content data structure for lightingdesign that is populated with machine learning optimized factors thatcontribute to emotional effects of lighting and having a lighting spacemodel of an environment in an augmented reality interface through whicha user controls lights placed in the environment via virtual lightingcontrol features presented in the augmented reality interface. A methodof using emotional filters for lighting design having an emotionalcontent data structure for lighting design that is populated withmachine learning optimized factors that contribute to emotional effectsof lighting and having use of an indirect illumination collectionfacility that is disposable throughout a three-dimensional regionproximal to a light source that captures a plurality ofmulti-dimensional light source illumination images. A method of usingemotional filters for lighting design having an emotional content datastructure for lighting design that is populated with machine learningoptimized factors that contribute to emotional effects of lighting andhaving a custom tuning profile that coordinates changes in color andlight output of a programmable light source to match lightingcharacteristics of a legacy light fixture. A method of using emotionalfilters for lighting design having an emotional content data structurefor lighting design that is populated with machine learning optimizedfactors that contribute to emotional effects of lighting and having anindirect illumination collection facility that captures near fieldmulti-dimensional illumination indirectly from a light source by varyingdistance and orientation of the collection facility relative to thelight source throughout the near field space of the light source. Amethod of using emotional filters for lighting design having anemotional content data structure for lighting design that is populatedwith machine learning optimized factors that contribute to emotionaleffects of lighting and having algorithms for calculating light quality,intensity, color range, and spectral characteristic metrics of a lightsource from illumination values collected at a plurality of theta andphi differentiated positions in the light source's near field space. Amethod of using emotional filters for lighting design having anemotional content data structure for lighting design that is populatedwith machine learning optimized factors that contribute to emotionaleffects of lighting and having a light source emulating devicepositioned by a user in a three-dimensional space that is presented inan augmented reality lighting design system that uses the position andorientation of the emulating device to model a lighting effect in thespace. A method of using emotional filters for lighting design having anemotional content data structure for lighting design that is populatedwith machine learning optimized factors that contribute to emotionaleffects of lighting and having a color map to control a multi-channellight thereby producing consistent color across a range of color modesby adjusting a plurality of the channels based on the color map. Amethod of using emotional filters for lighting design having anemotional content data structure for lighting design that is populatedwith machine learning optimized factors that contribute to emotionaleffects of lighting and having a near field dataset characterizing alight source, generating a geometric model from the data set thatfacilitates modeling an impact of the light source on objects disposedin the near field. A method of using emotional filters for lightingdesign having an emotional content data structure for lighting designthat is populated with machine learning optimized factors thatcontribute to emotional effects of lighting and having algorithms thatconstruct three-dimensional illumination data sets from a plurality ofdistinct two-dimensional illumination data arrays that are capturedthroughout a near field space of a light source. A method of usingemotional filters for lighting design having an emotional content datastructure for lighting design that is populated with machine learningoptimized factors that contribute to emotional effects of lighting andhaving a system for adapting light controls based on machine learningfrom data representing user activity including user time zone travel,user activities in an environment illuminated by a light beingcontrolled, wearable sensor biomarker user data, and feedback from usersin the environment. A method of using emotional filters for lightingdesign having an emotional content data structure for lighting designthat is populated with machine learning optimized factors thatcontribute to emotional effects of lighting and having a renderingpipeline in a lighting design system that allocates rendering to devicesin the pipeline based on a correspondence between the device renderingcapability and a resolution of image content to be rendered. A method ofusing emotional filters for lighting design having an emotional contentdata structure for lighting design that is populated with machinelearning optimized factors that contribute to emotional effects oflighting and having coordinated control of uplights and downlights toachieve a lighting effect in an environment that mimics a sky color fora given time of day. A method having techniques for determining lightsource characteristics from a desired lighting effect data set that areused to identify candidate light sources for producing the desiredlighting effect. A method having techniques for determining light sourcecharacteristics from a desired lighting effect data set that are used toidentify candidate light sources for producing the desired lightingeffect and having a lighting space model configured with lighting modelsof machine learning-based architectural elements detected in a pointcloud representative of a physical environment. A method havingtechniques for determining light source characteristics from a desiredlighting effect data set that are used to identify candidate lightsources for producing the desired lighting effect and having machinelearning generate a lighting space model of an environment from a pointcloud representation of the environment for use in an augmented realitylighting design interface and using the lighting space model to generatea floor plan of the space based on light sources placed by a user in theaugmented reality interface. A method having techniques for determininglight source characteristics from a desired lighting effect data setthat are used to identify candidate light sources for producing thedesired lighting effect and having a lighting space model of anenvironment in an augmented reality interface through which a usercontrols lights placed in the environment via virtual lighting controlfeatures presented in the augmented reality interface. A method havingtechniques for determining light source characteristics from a desiredlighting effect data set that are used to identify candidate lightsources for producing the desired lighting effect and having use of anindirect illumination collection facility that is disposable throughouta three-dimensional region proximal to a light source that captures aplurality of multi-dimensional light source illumination images. Amethod having techniques for determining light source characteristicsfrom a desired lighting effect data set that are used to identifycandidate light sources for producing the desired lighting effect andhaving a custom tuning profile that coordinates changes in color andlight output of a programmable light source to match lightingcharacteristics of a legacy light fixture. A method having techniquesfor determining light source characteristics from a desired lightingeffect data set that are used to identify candidate light sources forproducing the desired lighting effect and having an indirectillumination collection facility that captures near fieldmulti-dimensional illumination indirectly from a light source by varyingdistance and orientation of the collection facility relative to thelight source throughout the near field space of the light source. Amethod having techniques for determining light source characteristicsfrom a desired lighting effect data set that are used to identifycandidate light sources for producing the desired lighting effect andhaving algorithms for calculating light quality, intensity, color range,and spectral characteristic metrics of a light source from illuminationvalues collected at a plurality of theta and phi differentiatedpositions in the light source's near field space. A method havingtechniques for determining light source characteristics from a desiredlighting effect data set that are used to identify candidate lightsources for producing the desired lighting effect and having a lightsource emulating device positioned by a user in a three-dimensionalspace that is presented in an augmented reality lighting design systemthat uses the position and orientation of the emulating device to modela lighting effect in the space. A method having techniques fordetermining light source characteristics from a desired lighting effectdata set that are used to identify candidate light sources for producingthe desired lighting effect and having a color map to control amulti-channel light thereby producing consistent color across a range ofcolor modes by adjusting a plurality of the channels based on the colormap. A method having techniques for determining light sourcecharacteristics from a desired lighting effect data set that are used toidentify candidate light sources for producing the desired lightingeffect and having a near field dataset characterizing a light source,generating a geometric model from the data set that facilitates modelingan impact of the light source on objects disposed in the near field. Amethod having techniques for determining light source characteristicsfrom a desired lighting effect data set that are used to identifycandidate light sources for producing the desired lighting effect andhaving algorithms that construct three-dimensional illumination datasets from a plurality of distinct two-dimensional illumination dataarrays that are captured throughout a near field space of a lightsource. A method having techniques for determining light sourcecharacteristics from a desired lighting effect data set that are used toidentify candidate light sources for producing the desired lightingeffect and having a system for adapting light controls based on machinelearning from data representing user activity including user time zonetravel, user activities in an environment illuminated by a light beingcontrolled, wearable sensor biomarker user data, and feedback from usersin the environment. A method having techniques for determining lightsource characteristics from a desired lighting effect data set that areused to identify candidate light sources for producing the desiredlighting effect and having a rendering pipeline in a lighting designsystem that allocates rendering to devices in the pipeline based on acorrespondence between the device rendering capability and a resolutionof image content to be rendered. A method having techniques fordetermining light source characteristics from a desired lighting effectdata set that are used to identify candidate light sources for producingthe desired lighting effect and having coordinated control of uplightsand downlights to achieve a lighting effect in an environment thatmimics a sky color for a given time of day

A method of configuring a three-dimensional space for lightingsimulation having a lighting space model configured with lighting modelsof machine learning-based architectural elements detected in a pointcloud representative of a physical environment. A method of configuringa three-dimensional space for lighting simulation having a lightingspace model configured with lighting models of machine learning-basedarchitectural elements detected in a point cloud representative of aphysical environment and having machine learning generate a lightingspace model of an environment from a point cloud representation of theenvironment for use in an augmented reality lighting design interfaceand using the lighting space model to generate a floor plan of the spacebased on light sources placed by a user in the augmented realityinterface. A method of configuring a three-dimensional space forlighting simulation having a lighting space model configured withlighting models of machine learning-based architectural elementsdetected in a point cloud representative of a physical environment. andhaving a lighting space model of an environment in an augmented realityinterface through which a user controls lights placed in the environmentvia virtual lighting control features presented in the augmented realityinterface. A method of configuring a three-dimensional space forlighting simulation having a lighting space model configured withlighting models of machine learning-based architectural elementsdetected in a point cloud representative of a physical environment. andhaving use of an indirect illumination collection facility that isdisposable throughout a three-dimensional region proximal to a lightsource that captures a plurality of multi-dimensional light sourceillumination images. A method of configuring a three-dimensional spacefor lighting simulation having a lighting space model configured withlighting models of machine learning-based architectural elementsdetected in a point cloud representative of a physical environment. andhaving a custom tuning profile that coordinates changes in color andlight output of a programmable light source to match lightingcharacteristics of a legacy light fixture. A method of configuring athree-dimensional space for lighting simulation having a lighting spacemodel configured with lighting models of machine learning-basedarchitectural elements detected in a point cloud representative of aphysical environment. and having an indirect illumination collectionfacility that captures near field multi-dimensional illuminationindirectly from a light source by varying distance and orientation ofthe collection facility relative to the light source throughout the nearfield space of the light source. A method of configuring athree-dimensional space for lighting simulation having a lighting spacemodel configured with lighting models of machine learning-basedarchitectural elements detected in a point cloud representative of aphysical environment. and having algorithms for calculating lightquality, intensity, color range, and spectral characteristic metrics ofa light source from illumination values collected at a plurality oftheta and phi differentiated positions in the light source's near fieldspace. A method of configuring a three-dimensional space for lightingsimulation having a lighting space model configured with lighting modelsof machine learning-based architectural elements detected in a pointcloud representative of a physical environment. and having a lightsource emulating device positioned by a user in a three-dimensionalspace that is presented in an augmented reality lighting design systemthat uses the position and orientation of the emulating device to modela lighting effect in the space. A method of configuring athree-dimensional space for lighting simulation having a lighting spacemodel configured with lighting models of machine learning-basedarchitectural elements detected in a point cloud representative of aphysical environment. and having a color map to control a multi-channellight thereby producing consistent color across a range of color modesby adjusting a plurality of the channels based on the color map. Amethod of configuring a three-dimensional space for lighting simulationhaving a lighting space model configured with lighting models of machinelearning-based architectural elements detected in a point cloudrepresentative of a physical environment. and having a near fielddataset characterizing a light source, generating a geometric model fromthe data set that facilitates modeling an impact of the light source onobjects disposed in the near field. A method of configuring athree-dimensional space for lighting simulation having a lighting spacemodel configured with lighting models of machine learning-basedarchitectural elements detected in a point cloud representative of aphysical environment. and having algorithms that constructthree-dimensional illumination data sets from a plurality of distincttwo-dimensional illumination data arrays that are captured throughout anear field space of a light source. A method of configuring athree-dimensional space for lighting simulation having a lighting spacemodel configured with lighting models of machine learning-basedarchitectural elements detected in a point cloud representative of aphysical environment. and having a system for adapting light controlsbased on machine learning from data representing user activity includinguser time zone travel, user activities in an environment illuminated bya light being controlled, wearable sensor biomarker user data, andfeedback from users in the environment. A method of configuring athree-dimensional space for lighting simulation having a lighting spacemodel configured with lighting models of machine learning-basedarchitectural elements detected in a point cloud representative of aphysical environment. and having a rendering pipeline in a lightingdesign system that allocates rendering to devices in the pipeline basedon a correspondence between the device rendering capability and aresolution of image content to be rendered. A method of configuring athree-dimensional space for lighting simulation having a lighting spacemodel configured with lighting models of machine learning-basedarchitectural elements detected in a point cloud representative of aphysical environment. and having coordinated control of uplights anddownlights to achieve a lighting effect in an environment that mimics asky color for a given time of day.

A method for planning lighting in an augmented reality display havingmachine learning generate a lighting space model of an environment froma point cloud representation of the environment for use in an augmentedreality lighting design interface and using the lighting space model togenerate a floor plan of the space based on light sources placed by auser in the augmented reality interface. A method for planning lightingin an augmented reality display having machine learning generate alighting space model of an environment from a point cloud representationof the environment for use in an augmented reality lighting designinterface and using the lighting space model to generate a floor plan ofthe space based on light sources placed by a user in the augmentedreality interface and having a lighting space model of an environment inan augmented reality interface through which a user controls lightsplaced in the environment via virtual lighting control featurespresented in the augmented reality interface. A method for planninglighting in an augmented reality display having machine learninggenerate a lighting space model of an environment from a point cloudrepresentation of the environment for use in an augmented realitylighting design interface and using the lighting space model to generatea floor plan of the space based on light sources placed by a user in theaugmented reality interface and having use of an indirect illuminationcollection facility that is disposable throughout a three-dimensionalregion proximal to a light source that captures a plurality ofmulti-dimensional light source illumination images. A method forplanning lighting in an augmented reality display having machinelearning generate a lighting space model of an environment from a pointcloud representation of the environment for use in an augmented realitylighting design interface and using the lighting space model to generatea floor plan of the space based on light sources placed by a user in theaugmented reality interface and having a custom tuning profile thatcoordinates changes in color and light output of a programmable lightsource to match lighting characteristics of a legacy light fixture. Amethod for planning lighting in an augmented reality display havingmachine learning generate a lighting space model of an environment froma point cloud representation of the environment for use in an augmentedreality lighting design interface and using the lighting space model togenerate a floor plan of the space based on light sources placed by auser in the augmented reality interface and having an indirectillumination collection facility that captures near fieldmulti-dimensional illumination indirectly from a light source by varyingdistance and orientation of the collection facility relative to thelight source throughout the near field space of the light source. Amethod for planning lighting in an augmented reality display havingmachine learning generate a lighting space model of an environment froma point cloud representation of the environment for use in an augmentedreality lighting design interface and using the lighting space model togenerate a floor plan of the space based on light sources placed by auser in the augmented reality interface and having algorithms forcalculating light quality, intensity, color range, and spectralcharacteristic metrics of a light source from illumination valuescollected at a plurality of theta and phi differentiated positions inthe light source's near field space. A method for planning lighting inan augmented reality display having machine learning generate a lightingspace model of an environment from a point cloud representation of theenvironment for use in an augmented reality lighting design interfaceand using the lighting space model to generate a floor plan of the spacebased on light sources placed by a user in the augmented realityinterface and having a light source emulating device positioned by auser in a three-dimensional space that is presented in an augmentedreality lighting design system that uses the position and orientation ofthe emulating device to model a lighting effect in the space. A methodfor planning lighting in an augmented reality display having machinelearning generate a lighting space model of an environment from a pointcloud representation of the environment for use in an augmented realitylighting design interface and using the lighting space model to generatea floor plan of the space based on light sources placed by a user in theaugmented reality interface and having a color map to control amulti-channel light thereby producing consistent color across a range ofcolor modes by adjusting a plurality of the channels based on the colormap. A method for planning lighting in an augmented reality displayhaving machine learning generate a lighting space model of anenvironment from a point cloud representation of the environment for usein an augmented reality lighting design interface and using the lightingspace model to generate a floor plan of the space based on light sourcesplaced by a user in the augmented reality interface and having a nearfield dataset characterizing a light source, generating a geometricmodel from the data set that facilitates modeling an impact of the lightsource on objects disposed in the near field. A method for planninglighting in an augmented reality display having machine learninggenerate a lighting space model of an environment from a point cloudrepresentation of the environment for use in an augmented realitylighting design interface and using the lighting space model to generatea floor plan of the space based on light sources placed by a user in theaugmented reality interface and having algorithms that constructthree-dimensional illumination data sets from a plurality of distincttwo-dimensional illumination data arrays that are captured throughout anear field space of a light source. A method for planning lighting in anaugmented reality display having machine learning generate a lightingspace model of an environment from a point cloud representation of theenvironment for use in an augmented reality lighting design interfaceand using the lighting space model to generate a floor plan of the spacebased on light sources placed by a user in the augmented realityinterface and having a system for adapting light controls based onmachine learning from data representing user activity including usertime zone travel, user activities in an environment illuminated by alight being controlled, wearable sensor biomarker user data, andfeedback from users in the environment. A method for planning lightingin an augmented reality display having machine learning generate alighting space model of an environment from a point cloud representationof the environment for use in an augmented reality lighting designinterface and using the lighting space model to generate a floor plan ofthe space based on light sources placed by a user in the augmentedreality interface and having a rendering pipeline in a lighting designsystem that allocates rendering to devices in the pipeline based on acorrespondence between the device rendering capability and a resolutionof image content to be rendered. A method for planning lighting in anaugmented reality display having machine learning generate a lightingspace model of an environment from a point cloud representation of theenvironment for use in an augmented reality lighting design interfaceand using the lighting space model to generate a floor plan of the spacebased on light sources placed by a user in the augmented realityinterface and having coordinated control of uplights and downlights toachieve a lighting effect in an environment that mimics a sky color fora given time of day.

A method of control of modeled light sources in an augmented realityinterface having a lighting space model of an environment in anaugmented reality interface through which a user controls lights placedin the environment via virtual lighting control features presented inthe augmented reality interface. A method of control of modeled lightsources in an augmented reality interface having a lighting space modelof an environment in an augmented reality interface through which a usercontrols lights placed in the environment via virtual lighting controlfeatures presented in the augmented reality interface and having use ofan indirect illumination collection facility that is disposablethroughout a three-dimensional region proximal to a light source thatcaptures a plurality of multi-dimensional light source illuminationimages. A method of control of modeled light sources in an augmentedreality interface having a lighting space model of an environment in anaugmented reality interface through which a user controls lights placedin the environment via virtual lighting control features presented inthe augmented reality interface and having a custom tuning profile thatcoordinates changes in color and light output of a programmable lightsource to match lighting characteristics of a legacy light fixture. Amethod of control of modeled light sources in an augmented realityinterface having a lighting space model of an environment in anaugmented reality interface through which a user controls lights placedin the environment via virtual lighting control features presented inthe augmented reality interface and having an indirect illuminationcollection facility that captures near field multi-dimensionalillumination indirectly from a light source by varying distance andorientation of the collection facility relative to the light sourcethroughout the near field space of the light source. A method of controlof modeled light sources in an augmented reality interface having alighting space model of an environment in an augmented reality interfacethrough which a user controls lights placed in the environment viavirtual lighting control features presented in the augmented realityinterface and having algorithms for calculating light quality,intensity, color range, and spectral characteristic metrics of a lightsource from illumination values collected at a plurality of theta andphi differentiated positions in the light source's near field space. Amethod of control of modeled light sources in an augmented realityinterface having a lighting space model of an environment in anaugmented reality interface through which a user controls lights placedin the environment via virtual lighting control features presented inthe augmented reality interface and having a light source emulatingdevice positioned by a user in a three-dimensional space that ispresented in an augmented reality lighting design system that uses theposition and orientation of the emulating device to model a lightingeffect in the space. A method of control of modeled light sources in anaugmented reality interface having a lighting space model of anenvironment in an augmented reality interface through which a usercontrols lights placed in the environment via virtual lighting controlfeatures presented in the augmented reality interface and having a colormap to control a multi-channel light thereby producing consistent coloracross a range of color modes by adjusting a plurality of the channelsbased on the color map. A method of control of modeled light sources inan augmented reality interface having a lighting space model of anenvironment in an augmented reality interface through which a usercontrols lights placed in the environment via virtual lighting controlfeatures presented in the augmented reality interface and having a nearfield dataset characterizing a light source, generating a geometricmodel from the data set that facilitates modeling an impact of the lightsource on objects disposed in the near field. A method of control ofmodeled light sources in an augmented reality interface having alighting space model of an environment in an augmented reality interfacethrough which a user controls lights placed in the environment viavirtual lighting control features presented in the augmented realityinterface and having algorithms that construct three-dimensionalillumination data sets from a plurality of distinct two-dimensionalillumination data arrays that are captured throughout a near field spaceof a light source. A method of control of modeled light sources in anaugmented reality interface having a lighting space model of anenvironment in an augmented reality interface through which a usercontrols lights placed in the environment via virtual lighting controlfeatures presented in the augmented reality interface and having asystem for adapting light controls based on machine learning from datarepresenting user activity including user time zone travel, useractivities in an environment illuminated by a light being controlled,wearable sensor biomarker user data, and feedback from users in theenvironment. A method of control of modeled light sources in anaugmented reality interface having a lighting space model of anenvironment in an augmented reality interface through which a usercontrols lights placed in the environment via virtual lighting controlfeatures presented in the augmented reality interface and having arendering pipeline in a lighting design system that allocates renderingto devices in the pipeline based on a correspondence between the devicerendering capability and a resolution of image content to be rendered. Amethod of control of modeled light sources in an augmented realityinterface having a lighting space model of an environment in anaugmented reality interface through which a user controls lights placedin the environment via virtual lighting control features presented inthe augmented reality interface and having coordinated control ofuplights and downlights to achieve a lighting effect in an environmentthat mimics a sky color for a given time of day.

A method of generating a data structure that characterizes the nearfield illumination pattern generated by a light source having use of anindirect illumination collection facility that is disposable throughouta three-dimensional region proximal to a light source that captures aplurality of multi-dimensional light source illumination images. Amethod of generating a data structure that characterizes the near fieldillumination pattern generated by a light source having use of anindirect illumination collection facility that is disposable throughouta three-dimensional region proximal to a light source that captures aplurality of multi-dimensional light source illumination images andhaving a custom tuning profile that coordinates changes in color andlight output of a programmable light source to match lightingcharacteristics of a legacy light fixture. A method of generating a datastructure that characterizes the near field illumination patterngenerated by a light source having use of an indirect illuminationcollection facility that is disposable throughout a three-dimensionalregion proximal to a light source that captures a plurality ofmulti-dimensional light source illumination images and having anindirect illumination collection facility that captures near fieldmulti-dimensional illumination indirectly from a light source by varyingdistance and orientation of the collection facility relative to thelight source throughout the near field space of the light source. Amethod of generating a data structure that characterizes the near fieldillumination pattern generated by a light source having use of anindirect illumination collection facility that is disposable throughouta three-dimensional region proximal to a light source that captures aplurality of multi-dimensional light source illumination images andhaving algorithms for calculating light quality, intensity, color range,and spectral characteristic metrics of a light source from illuminationvalues collected at a plurality of theta and phi differentiatedpositions in the light source's near field space. A method of generatinga data structure that characterizes the near field illumination patterngenerated by a light source having use of an indirect illuminationcollection facility that is disposable throughout a three-dimensionalregion proximal to a light source that captures a plurality ofmulti-dimensional light source illumination images and having a lightsource emulating device positioned by a user in a three-dimensionalspace that is presented in an augmented reality lighting design systemthat uses the position and orientation of the emulating device to modela lighting effect in the space. A method of generating a data structurethat characterizes the near field illumination pattern generated by alight source having use of an indirect illumination collection facilitythat is disposable throughout a three-dimensional region proximal to alight source that captures a plurality of multi-dimensional light sourceillumination images and having a color map to control a multi-channellight thereby producing consistent color across a range of color modesby adjusting a plurality of the channels based on the color map. Amethod of generating a data structure that characterizes the near fieldillumination pattern generated by a light source having use of anindirect illumination collection facility that is disposable throughouta three-dimensional region proximal to a light source that captures aplurality of multi-dimensional light source illumination images andhaving a near field dataset characterizing a light source, generating ageometric model from the data set that facilitates modeling an impact ofthe light source on objects disposed in the near field. A method ofgenerating a data structure that characterizes the near fieldillumination pattern generated by a light source having use of anindirect illumination collection facility that is disposable throughouta three-dimensional region proximal to a light source that captures aplurality of multi-dimensional light source illumination images andhaving algorithms that construct three-dimensional illumination datasets from a plurality of distinct two-dimensional illumination dataarrays that are captured throughout a near field space of a lightsource. A method of generating a data structure that characterizes thenear field illumination pattern generated by a light source having useof an indirect illumination collection facility that is disposablethroughout a three-dimensional region proximal to a light source thatcaptures a plurality of multi-dimensional light source illuminationimages and having a system for adapting light controls based on machinelearning from data representing user activity including user time zonetravel, user activities in an environment illuminated by a light beingcontrolled, wearable sensor biomarker user data, and feedback from usersin the environment. A method of generating a data structure thatcharacterizes the near field illumination pattern generated by a lightsource having use of an indirect illumination collection facility thatis disposable throughout a three-dimensional region proximal to a lightsource that captures a plurality of multi-dimensional light sourceillumination images and having a rendering pipeline in a lighting designsystem that allocates rendering to devices in the pipeline based on acorrespondence between the device rendering capability and a resolutionof image content to be rendered. A method of generating a data structurethat characterizes the near field illumination pattern generated by alight source having use of an indirect illumination collection facilitythat is disposable throughout a three-dimensional region proximal to alight source that captures a plurality of multi-dimensional light sourceillumination images and having coordinated control of uplights anddownlights to achieve a lighting effect in an environment that mimics asky color for a given time of day.

A system for enabling custom tuning a lighting object having a customtuning profile that coordinates changes in color and light output of aprogrammable light source to match lighting characteristics of a legacylight fixture. A system for enabling custom tuning a lighting objecthaving a custom tuning profile that coordinates changes in color andlight output of a programmable light source to match lightingcharacteristics of a legacy light fixture and having an indirectillumination collection facility that captures near fieldmulti-dimensional illumination indirectly from a light source by varyingdistance and orientation of the collection facility relative to thelight source throughout the near field space of the light source. Asystem for enabling custom tuning a lighting object having a customtuning profile that coordinates changes in color and light output of aprogrammable light source to match lighting characteristics of a legacylight fixture and having algorithms for calculating light quality,intensity, color range, and spectral characteristic metrics of a lightsource from illumination values collected at a plurality of theta andphi differentiated positions in the light source's near field space. Asystem for enabling custom tuning a lighting object having a customtuning profile that coordinates changes in color and light output of aprogrammable light source to match lighting characteristics of a legacylight fixture and having a light source emulating device positioned by auser in a three-dimensional space that is presented in an augmentedreality lighting design system that uses the position and orientation ofthe emulating device to model a lighting effect in the space. A systemfor enabling custom tuning a lighting object having a custom tuningprofile that coordinates changes in color and light output of aprogrammable light source to match lighting characteristics of a legacylight fixture and having a color map to control a multi-channel lightthereby producing consistent color across a range of color modes byadjusting a plurality of the channels based on the color map. A systemfor enabling custom tuning a lighting object having a custom tuningprofile that coordinates changes in color and light output of aprogrammable light source to match lighting characteristics of a legacylight fixture and having a near field dataset characterizing a lightsource, generating a geometric model from the data set that facilitatesmodeling an impact of the light source on objects disposed in the nearfield. A system for enabling custom tuning a lighting object having acustom tuning profile that coordinates changes in color and light outputof a programmable light source to match lighting characteristics of alegacy light fixture and having algorithms that constructthree-dimensional illumination data sets from a plurality of distincttwo-dimensional illumination data arrays that are captured throughout anear field space of a light source. A system for enabling custom tuninga lighting object having a custom tuning profile that coordinateschanges in color and light output of a programmable light source tomatch lighting characteristics of a legacy light fixture and having asystem for adapting light controls based on machine learning from datarepresenting user activity including user time zone travel, useractivities in an environment illuminated by a light being controlled,wearable sensor biomarker user data, and feedback from users in theenvironment. A system for enabling custom tuning a lighting objecthaving a custom tuning profile that coordinates changes in color andlight output of a programmable light source to match lightingcharacteristics of a legacy light fixture and having a renderingpipeline in a lighting design system that allocates rendering to devicesin the pipeline based on a correspondence between the device renderingcapability and a resolution of image content to be rendered. A systemfor enabling custom tuning a lighting object having a custom tuningprofile that coordinates changes in color and light output of aprogrammable light source to match lighting characteristics of a legacylight fixture and having coordinated control of uplights and downlightsto achieve a lighting effect in an environment that mimics a sky colorfor a given time of day.

A near-field characterization system having an indirect illuminationcollection facility that captures near field multi-dimensionalillumination indirectly from a light source by varying distance andorientation of the collection facility relative to the light sourcethroughout the near field space of the light source. A near-fieldcharacterization system having an indirect illumination collectionfacility that captures near field multi-dimensional illuminationindirectly from a light source by varying distance and orientation ofthe collection facility relative to the light source throughout the nearfield space of the light source and having algorithms for calculatinglight quality, intensity, color range, and spectral characteristicmetrics of a light source from illumination values collected at aplurality of theta and phi differentiated positions in the lightsource's near field space. A near-field characterization system havingan indirect illumination collection facility that captures near fieldmulti-dimensional illumination indirectly from a light source by varyingdistance and orientation of the collection facility relative to thelight source throughout the near field space of the light source andhaving a light source emulating device positioned by a user in athree-dimensional space that is presented in an augmented realitylighting design system that uses the position and orientation of theemulating device to model a lighting effect in the space. A near-fieldcharacterization system having an indirect illumination collectionfacility that captures near field multi-dimensional illuminationindirectly from a light source by varying distance and orientation ofthe collection facility relative to the light source throughout the nearfield space of the light source and having a color map to control amulti-channel light thereby producing consistent color across a range ofcolor modes by adjusting a plurality of the channels based on the colormap. A near-field characterization system having an indirectillumination collection facility that captures near fieldmulti-dimensional illumination indirectly from a light source by varyingdistance and orientation of the collection facility relative to thelight source throughout the near field space of the light source andhaving a near field dataset characterizing a light source, generating ageometric model from the data set that facilitates modeling an impact ofthe light source on objects disposed in the near field. A near-fieldcharacterization system having an indirect illumination collectionfacility that captures near field multi-dimensional illuminationindirectly from a light source by varying distance and orientation ofthe collection facility relative to the light source throughout the nearfield space of the light source and having algorithms that constructthree-dimensional illumination data sets from a plurality of distincttwo-dimensional illumination data arrays that are captured throughout anear field space of a light source. A near-field characterization systemhaving an indirect illumination collection facility that captures nearfield multi-dimensional illumination indirectly from a light source byvarying distance and orientation of the collection facility relative tothe light source throughout the near field space of the light source andhaving a system for adapting light controls based on machine learningfrom data representing user activity including user time zone travel,user activities in an environment illuminated by a light beingcontrolled, wearable sensor biomarker user data, and feedback from usersin the environment. A near-field characterization system having anindirect illumination collection facility that captures near fieldmulti-dimensional illumination indirectly from a light source by varyingdistance and orientation of the collection facility relative to thelight source throughout the near field space of the light source andhaving a rendering pipeline in a lighting design system that allocatesrendering to devices in the pipeline based on a correspondence betweenthe device rendering capability and a resolution of image content to berendered. A near-field characterization system having an indirectillumination collection facility that captures near fieldmulti-dimensional illumination indirectly from a light source by varyingdistance and orientation of the collection facility relative to thelight source throughout the near field space of the light source andhaving coordinated control of uplights and downlights to achieve alighting effect in an environment that mimics a sky color for a giventime of day

A method of near field metrics for evaluating light sources havingalgorithms for calculating light quality, intensity, color range, andspectral characteristic metrics of a light source from illuminationvalues collected at a plurality of theta and phi differentiatedpositions in the light source's near field space. A method of near fieldmetrics for evaluating light sources having algorithms for calculatinglight quality, intensity, color range, and spectral characteristicmetrics of a light source from illumination values collected at aplurality of theta and phi differentiated positions in the lightsource's near field space and having a light source emulating devicepositioned by a user in a three-dimensional space that is presented inan augmented reality lighting design system that uses the position andorientation of the emulating device to model a lighting effect in thespace. A method of near field metrics for evaluating light sourceshaving algorithms for calculating light quality, intensity, color range,and spectral characteristic metrics of a light source from illuminationvalues collected at a plurality of theta and phi differentiatedpositions in the light source's near field space and having a color mapto control a multi-channel light thereby producing consistent coloracross a range of color modes by adjusting a plurality of the channelsbased on the color map. A method of near field metrics for evaluatinglight sources having algorithms for calculating light quality,intensity, color range, and spectral characteristic metrics of a lightsource from illumination values collected at a plurality of theta andphi differentiated positions in the light source's near field space andhaving a near field dataset characterizing a light source, generating ageometric model from the data set that facilitates modeling an impact ofthe light source on objects disposed in the near field. A method of nearfield metrics for evaluating light sources having algorithms forcalculating light quality, intensity, color range, and spectralcharacteristic metrics of a light source from illumination valuescollected at a plurality of theta and phi differentiated positions inthe light source's near field space and having algorithms that constructthree-dimensional illumination data sets from a plurality of distincttwo-dimensional illumination data arrays that are captured throughout anear field space of a light source. A method of near field metrics forevaluating light sources having algorithms for calculating lightquality, intensity, color range, and spectral characteristic metrics ofa light source from illumination values collected at a plurality oftheta and phi differentiated positions in the light source's near fieldspace and having a system for adapting light controls based on machinelearning from data representing user activity including user time zonetravel, user activities in an environment illuminated by a light beingcontrolled, wearable sensor biomarker user data, and feedback from usersin the environment. A method of near field metrics for evaluating lightsources having algorithms for calculating light quality, intensity,color range, and spectral characteristic metrics of a light source fromillumination values collected at a plurality of theta and phidifferentiated positions in the light source's near field space andhaving a rendering pipeline in a lighting design system that allocatesrendering to devices in the pipeline based on a correspondence betweenthe device rendering capability and a resolution of image content to berendered. A method of near field metrics for evaluating light sourceshaving algorithms for calculating light quality, intensity, color range,and spectral characteristic metrics of a light source from illuminationvalues collected at a plurality of theta and phi differentiatedpositions in the light source's near field space and having coordinatedcontrol of uplights and downlights to achieve a lighting effect in anenvironment that mimics a sky color for a given time of day.

A method of augmented reality-based lighting design having a lightsource emulating device positioned by a user in a three-dimensionalspace that is presented in an augmented reality lighting design systemthat uses the position and orientation of the emulating device to modela lighting effect in the space. A method of augmented reality-basedlighting design having a light source emulating device positioned by auser in a three-dimensional space that is presented in an augmentedreality lighting design system that uses the position and orientation ofthe emulating device to model a lighting effect in the space and havinga color map to control a multi-channel light thereby producingconsistent color across a range of color modes by adjusting a pluralityof the channels based on the color map. A method of augmentedreality-based lighting design having a light source emulating devicepositioned by a user in a three-dimensional space that is presented inan augmented reality lighting design system that uses the position andorientation of the emulating device to model a lighting effect in thespace and having a near field dataset characterizing a light source,generating a geometric model from the data set that facilitates modelingan impact of the light source on objects disposed in the near field. Amethod of augmented reality-based lighting design having a light sourceemulating device positioned by a user in a three-dimensional space thatis presented in an augmented reality lighting design system that usesthe position and orientation of the emulating device to model a lightingeffect in the space and having algorithms that constructthree-dimensional illumination data sets from a plurality of distincttwo-dimensional illumination data arrays that are captured throughout anear field space of a light source. A method of augmented reality-basedlighting design having a light source emulating device positioned by auser in a three-dimensional space that is presented in an augmentedreality lighting design system that uses the position and orientation ofthe emulating device to model a lighting effect in the space and havinga system for adapting light controls based on machine learning from datarepresenting user activity including user time zone travel, useractivities in an environment illuminated by a light being controlled,wearable sensor biomarker user data, and feedback from users in theenvironment. A method of augmented reality-based lighting design havinga light source emulating device positioned by a user in athree-dimensional space that is presented in an augmented realitylighting design system that uses the position and orientation of theemulating device to model a lighting effect in the space and having arendering pipeline in a lighting design system that allocates renderingto devices in the pipeline based on a correspondence between the devicerendering capability and a resolution of image content to be rendered. Amethod of augmented reality-based lighting design having a light sourceemulating device positioned by a user in a three-dimensional space thatis presented in an augmented reality lighting design system that usesthe position and orientation of the emulating device to model a lightingeffect in the space and having coordinated control of uplights anddownlights to achieve a lighting effect in an environment that mimics asky color for a given time of day.

A method of producing a color tuning curve having a color map to controla multi-channel light thereby producing consistent color across a rangeof color modes by adjusting a plurality of the channels based on thecolor map. A method of producing a color tuning curve having a color mapto control a multi-channel light thereby producing consistent coloracross a range of color modes by adjusting a plurality of the channelsbased on the color map and having a near field dataset characterizing alight source, generating a geometric model from the data set thatfacilitates modeling an impact of the light source on objects disposedin the near field. A method of producing a color tuning curve having acolor map to control a multi-channel light thereby producing consistentcolor across a range of color modes by adjusting a plurality of thechannels based on the color map and having algorithms that constructthree-dimensional illumination data sets from a plurality of distincttwo-dimensional illumination data arrays that are captured throughout anear field space of a light source. A method of producing a color tuningcurve having a color map to control a multi-channel light therebyproducing consistent color across a range of color modes by adjusting aplurality of the channels based on the color map and having a system foradapting light controls based on machine learning from data representinguser activity including user time zone travel, user activities in anenvironment illuminated by a light being controlled, wearable sensorbiomarker user data, and feedback from users in the environment. Amethod of producing a color tuning curve having a color map to control amulti-channel light thereby producing consistent color across a range ofcolor modes by adjusting a plurality of the channels based on the colormap and having a rendering pipeline in a lighting design system thatallocates rendering to devices in the pipeline based on a correspondencebetween the device rendering capability and a resolution of imagecontent to be rendered. A method of producing a color tuning curvehaving a color map to control a multi-channel light thereby producingconsistent color across a range of color modes by adjusting a pluralityof the channels based on the color map and having coordinated control ofuplights and downlights to achieve a lighting effect in an environmentthat mimics a sky color for a given time of day.

A method of model-based rendering near-field effects of a light sourcehaving a near field dataset characterizing a light source, generating ageometric model from the data set that facilitates modeling an impact ofthe light source on objects disposed in the near field. A method ofmodel-based rendering near-field effects of a light source having a nearfield dataset characterizing a light source, generating a geometricmodel from the data set that facilitates modeling an impact of the lightsource on objects disposed in the near field and having algorithms thatconstruct three-dimensional illumination data sets from a plurality ofdistinct two-dimensional illumination data arrays that are capturedthroughout a near field space of a light source. A method of model-basedrendering near-field effects of a light source having a near fielddataset characterizing a light source, generating a geometric model fromthe data set that facilitates modeling an impact of the light source onobjects disposed in the near field and having a system for adaptinglight controls based on machine learning from data representing useractivity including user time zone travel, user activities in anenvironment illuminated by a light being controlled, wearable sensorbiomarker user data, and feedback from users in the environment. Amethod of model-based rendering near-field effects of a light sourcehaving a near field dataset characterizing a light source, generating ageometric model from the data set that facilitates modeling an impact ofthe light source on objects disposed in the near field and having arendering pipeline in a lighting design system that allocates renderingto devices in the pipeline based on a correspondence between the devicerendering capability and a resolution of image content to be rendered. Amethod of model-based rendering near-field effects of a light sourcehaving a near field dataset characterizing a light source, generating ageometric model from the data set that facilitates modeling an impact ofthe light source on objects disposed in the near field and havingcoordinated control of uplights and downlights to achieve a lightingeffect in an environment that mimics a sky color for a given time ofday.

A method for characterizing a near field illumination effect of a lightsource having algorithms that construct three-dimensional illuminationdata sets from a plurality of distinct two-dimensional illumination dataarrays that are captured throughout a near field space of a lightsource. A method for characterizing a near field illumination effect ofa light source having algorithms that construct three-dimensionalillumination data sets from a plurality of distinct two-dimensionalillumination data arrays that are captured throughout a near field spaceof a light source and having a system for adapting light controls basedon machine learning from data representing user activity including usertime zone travel, user activities in an environment illuminated by alight being controlled, wearable sensor biomarker user data, andfeedback from users in the environment. A method for characterizing anear field illumination effect of a light source having algorithms thatconstruct three-dimensional illumination data sets from a plurality ofdistinct two-dimensional illumination data arrays that are capturedthroughout a near field space of a light source and having a renderingpipeline in a lighting design system that allocates rendering to devicesin the pipeline based on a correspondence between the device renderingcapability and a resolution of image content to be rendered. A methodfor characterizing a near field illumination effect of a light sourcehaving algorithms that construct three-dimensional illumination datasets from a plurality of distinct two-dimensional illumination dataarrays that are captured throughout a near field space of a light sourceand having coordinated control of uplights and downlights to achieve alighting effect in an environment that mimics a sky color for a giventime of day.

A lighting control system having a system for adapting light controlsbased on machine learning from data representing user activity includinguser time zone travel, user activities in an environment illuminated bya light being controlled, wearable sensor biomarker user data, andfeedback from users in the environment. A lighting control system havinga system for adapting light controls based on machine learning from datarepresenting user activity including user time zone travel, useractivities in an environment illuminated by a light being controlled,wearable sensor biomarker user data, and feedback from users in theenvironment and having a rendering pipeline in a lighting design systemthat allocates rendering to devices in the pipeline based on acorrespondence between the device rendering capability and a resolutionof image content to be rendered. A lighting control system having asystem for adapting light controls based on machine learning from datarepresenting user activity including user time zone travel, useractivities in an environment illuminated by a light being controlled,wearable sensor biomarker user data, and feedback from users in theenvironment and having coordinated control of uplights and downlights toachieve a lighting effect in an environment that mimics a sky color fora given time of day.

A method having a rendering pipeline in a lighting design system thatallocates rendering to devices in the pipeline based on a correspondencebetween the device rendering capability and a resolution of imagecontent to be rendered. A method having a rendering pipeline in alighting design system that allocates rendering to devices in thepipeline based on a correspondence between the device renderingcapability and a resolution of image content to be rendered and havingcoordinated control of uplights and downlights to achieve a lightingeffect in an environment that mimics a sky color for a given time ofday. A method of illumination in an environment having coordinatedcontrol of uplights and downlights to achieve a lighting effect in anenvironment that mimics a sky color for a given time of day.

Detailed embodiments of the present disclosure are disclosed herein;however, it is to be understood that the disclosed embodiments aremerely exemplary of the disclosure, which may be embodied in variousforms. Therefore, specific structural and functional details disclosedherein are not to be interpreted as limiting, but merely as a basis forthe claims and as a representative basis for teaching one skilled in theart to variously employ the present disclosure in virtually anyappropriately detailed structure.

The terms “a” or “an,” as used herein, are defined as one or more thanone. The term “another,” as used herein, is defined as at least a secondor more. The terms “including” and/or “having,” as used herein, aredefined as comprising (i.e., open transition).

While only a few embodiments of the present disclosure have been shownand described, it will be obvious to those skilled in the art that manychanges and modifications may be made thereunto without departing fromthe spirit and scope of the present disclosure as described in thefollowing claims. All patent applications and patents, both foreign anddomestic, and all other publications referenced herein are incorporatedherein in their entireties to the full extent permitted by law.

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software, program codes,and/or instructions on a processor. The present disclosure may beimplemented as a method on the machine, as a system or apparatus as partof or in relation to the machine, or as a computer program productembodied in a computer readable medium executing on one or more of themachines. In embodiments, the processor may be part of a server, cloudserver, client, network infrastructure, mobile computing platform,stationary computing platform, or other computing platforms. A processormay be any kind of computational or processing device capable ofexecuting program instructions, codes, binary instructions and the like.The processor may be or may include a signal processor, digitalprocessor, embedded processor, microprocessor or any variant such as aco-processor (math co-processor, graphic co-processor, communicationco-processor and the like) and the like that may directly or indirectlyfacilitate execution of program code or program instructions storedthereon. In addition, the processor may enable execution of multipleprograms, threads, and codes. The threads may be executed simultaneouslyto enhance the performance of the processor and to facilitatesimultaneous operations of the application. By way of implementation,methods, program codes, program instructions and the like describedherein may be implemented in one or more thread. The thread may spawnother threads that may have assigned priorities associated with them;the processor may execute these threads based on priority or any otherorder based on instructions provided in the program code. The processor,or any machine utilizing one, may include non-transitory memory thatstores methods, codes, instructions and programs as described herein andelsewhere. The processor may access a non-transitory storage mediumthrough an interface that may store methods, codes, and instructions asdescribed herein and elsewhere. The storage medium associated with theprocessor for storing methods, programs, codes, program instructions orother type of instructions capable of being executed by the computing orprocessing device may include but may not be limited to one or more of aCD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache and thelike.

A processor may include one or more cores that may enhance speed andperformance of a multiprocessor. In embodiments, the process may be adual core processor, quad core processors, other chip-levelmultiprocessor and the like that combine two or more independent cores(called a die).

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software on a server,client, firewall, gateway, hub, router, or other such computer and/ornetworking hardware. The software program may be associated with aserver that may include a file server, print server, domain server,Internet server, intranet server, cloud server, and other variants suchas secondary server, host server, distributed server and the like. Theserver may include one or more of memories, processors, computerreadable media, storage media, ports (physical and virtual),communication devices, and interfaces capable of accessing otherservers, clients, machines, and devices through a wired or a wirelessmedium, and the like. The methods, programs, or codes as describedherein and elsewhere may be executed by the server. In addition, otherdevices required for execution of methods as described in thisapplication may be considered as a part of the infrastructure associatedwith the server.

The server may provide an interface to other devices including, withoutlimitation, clients, other servers, printers, database servers, printservers, file servers, communication servers, distributed servers,social networks, and the like. Additionally, this coupling and/orconnection may facilitate remote execution of program across thenetwork. The networking of some or all of these devices may facilitateparallel processing of a program or method at one or more locationwithout deviating from the scope of the disclosure. In addition, any ofthe devices attached to the server through an interface may include atleast one storage medium capable of storing methods, programs, codeand/or instructions. A central repository may provide programinstructions to be executed on different devices. In thisimplementation, the remote repository may act as a storage medium forprogram code, instructions, and programs.

The software program may be associated with a client that may include afile client, print client, domain client, Internet client, intranetclient and other variants such as secondary client, host client,distributed client and the like. The client may include one or more ofmemories, processors, computer readable media, storage media, ports(physical and virtual), communication devices, and interfaces capable ofaccessing other clients, servers, machines, and devices through a wiredor a wireless medium, and the like. The methods, programs, or codes asdescribed herein and elsewhere may be executed by the client. Inaddition, other devices required for execution of methods as describedin this application may be considered as a part of the infrastructureassociated with the client.

The client may provide an interface to other devices including, withoutlimitation, servers, other clients, printers, database servers, printservers, file servers, communication servers, distributed servers andthe like. Additionally, this coupling and/or connection may facilitateremote execution of program across the network. The networking of someor all of these devices may facilitate parallel processing of a programor method at one or more location without deviating from the scope ofthe disclosure. In addition, any of the devices attached to the clientthrough an interface may include at least one storage medium capable ofstoring methods, programs, applications, code and/or instructions. Acentral repository may provide program instructions to be executed ondifferent devices. In this implementation, the remote repository may actas a storage medium for program code, instructions, and programs.

The methods and systems described herein may be deployed in part or inwhole through network infrastructures. The network infrastructure mayinclude elements such as computing devices, servers, routers, hubs,firewalls, clients, personal computers, communication devices, routingdevices and other active and passive devices, modules and/or componentsas known in the art. The computing and/or non-computing device(s)associated with the network infrastructure may include, apart from othercomponents, a storage medium such as flash memory, buffer, stack, RAM,ROM and the like. The processes, methods, program codes, instructionsdescribed herein and elsewhere may be executed by one or more of thenetwork infrastructural elements. The methods and systems describedherein may be adapted for use with any kind of private, community, orhybrid cloud computing network or cloud computing environment, includingthose which involve features of software as a service (SaaS), platformas a service (PaaS), and/or infrastructure as a service (IaaS).

The methods, program codes, and instructions described herein andelsewhere may be implemented on a cellular network having multiplecells. The cellular network may either be frequency division multipleaccess (FDMA) network or code division multiple access (CDMA) network.The cellular network may include mobile devices, cell sites, basestations, repeaters, antennas, towers, and the like. The cell networkmay be a GSM, GPRS, 3G, EVDO, mesh, or other networks types.

The methods, program codes, and instructions described herein andelsewhere may be implemented on or through mobile devices. The mobiledevices may include navigation devices, cell phones, mobile phones,mobile personal digital assistants, laptops, palmtops, netbooks, pagers,electronic books readers, music players and the like. These devices mayinclude, apart from other components, a storage medium such as a flashmemory, buffer, RAM, ROM and one or more computing devices. Thecomputing devices associated with mobile devices may be enabled toexecute program codes, methods, and instructions stored thereon.Alternatively, the mobile devices may be configured to executeinstructions in collaboration with other devices. The mobile devices maycommunicate with base stations interfaced with servers and configured toexecute program codes. The mobile devices may communicate on apeer-to-peer network, mesh network, or other communications network. Theprogram code may be stored on the storage medium associated with theserver and executed by a computing device embedded within the server.The base station may include a computing device and a storage medium.The storage device may store program codes and instructions executed bythe computing devices associated with the base station.

The computer software, program codes, and/or instructions may be storedand/or accessed on machine readable media that may include: computercomponents, devices, and recording media that retain digital data usedfor computing for some interval of time; semiconductor storage known asrandom access memory (RAM); mass storage typically for more permanentstorage, such as optical discs, forms of magnetic storage like harddisks, tapes, drums, cards and other types; processor registers, cachememory, volatile memory, non-volatile memory; optical storage such asCD, DVD; removable media such as flash memory (e.g., USB sticks orkeys), floppy disks, magnetic tape, paper tape, punch cards, standaloneRAM disks, Zip drives, removable mass storage, off-line, and the like;other computer memory such as dynamic memory, static memory, read/writestorage, mutable storage, read only, random access, sequential access,location addressable, file addressable, content addressable, networkattached storage, storage area network, bar codes, magnetic ink, and thelike.

The methods and systems described herein may transform physical and/orintangible items from one state to another. The methods and systemsdescribed herein may also transform data representing physical and/orintangible items from one state to another.

The elements described and depicted herein, including in flowcharts andblock diagrams throughout the figures, imply logical boundaries betweenthe elements. However, according to software or hardware engineeringpractices, the depicted elements and the functions thereof may beimplemented on machines through computer executable media having aprocessor capable of executing program instructions stored thereon as amonolithic software structure, as standalone software modules, or asmodules that employ external routines, code, services, and so forth, orany combination of these, and all such implementations may be within thescope of the present disclosure. Examples of such machines may include,but may not be limited to, personal digital assistants, laptops,personal computers, mobile phones, other handheld computing devices,medical equipment, wired or wireless communication devices, transducers,chips, calculators, satellites, tablet PCs, electronic books, gadgets,electronic devices, devices having artificial intelligence, computingdevices, networking equipment, servers, routers and the like.Furthermore, the elements depicted in the flowchart and block diagramsor any other logical component may be implemented on a machine capableof executing program instructions. Thus, while the foregoing drawingsand descriptions set forth functional aspects of the disclosed systems,no particular arrangement of software for implementing these functionalaspects should be inferred from these descriptions unless explicitlystated or otherwise clear from the context. Similarly, it will beappreciated that the various steps identified and described above may bevaried and that the order of steps may be adapted to particularapplications of the techniques disclosed herein. All such variations andmodifications are intended to fall within the scope of this disclosure.As such, the depiction and/or description of an order for various stepsshould not be understood to require a particular order of execution forthose steps, unless required by a particular application, or explicitlystated or otherwise clear from the context.

The methods and/or processes described above, and steps associatedtherewith, may be realized in hardware, software or any combination ofhardware and software suitable for a particular application. Thehardware may include a general-purpose computer and/or dedicatedcomputing device or specific computing device or particular aspect orcomponent of a specific computing device. The processes may be realizedin one or more microprocessors, microcontrollers, embeddedmicrocontrollers, programmable digital signal processors or otherprogrammable devices, along with internal and/or external memory. Theprocesses may also, or instead, be embodied in an application specificintegrated circuit, a programmable gate array, programmable array logic,or any other device or combination of devices that may be configured toprocess electronic signals. It will further be appreciated that one ormore of the processes may be realized as a computer executable codecapable of being executed on a machine-readable medium. The computerexecutable code may be created using a structured programming languagesuch as C, an object oriented programming language such as C++, or anyother high-level or low-level programming language (including assemblylanguages, hardware description languages, and database programminglanguages and technologies) that may be stored, compiled or interpretedto run on one of the above devices, as well as heterogeneouscombinations of processors, processor architectures, or combinations ofdifferent hardware and software, or any other machine capable ofexecuting program instructions.

Thus, in one aspect, methods described above and combinations thereofmay be embodied in computer executable code that, when executing on oneor more computing devices, performs the steps thereof. In anotheraspect, the methods may be embodied in systems that perform the stepsthereof, and may be distributed across devices in a number of ways, orall of the functionality may be integrated into a dedicated, standalonedevice or other hardware. In another aspect, the means for performingthe steps associated with the processes described above may include anyof the hardware and/or software described above. All such permutationsand combinations are intended to fall within the scope of the presentdisclosure.

While the disclosure has been disclosed in connection with the preferredembodiments shown and described in detail, various modifications andimprovements thereon will become readily apparent to those skilled inthe art. Accordingly, the spirit and scope of the present disclosure isnot to be limited by the foregoing examples but is to be understood inthe broadest sense allowable by law.

The use of the terms “a” and “an” and “the” and similar referents in thecontext of describing the disclosure (especially in the context of thefollowing claims) is to be construed to cover both the singular and theplural unless otherwise indicated herein or clearly contradicted bycontext. The terms “comprising,” “having,” “including,” and “containing”are to be construed as open-ended terms (i.e., meaning “including, butnot limited to,”) unless otherwise noted. Recitations of ranges ofvalues herein are merely intended to serve as a shorthand method ofreferring individually to each separate value falling within the range,unless otherwise indicated herein, and each separate value isincorporated into the specification as if it were individually recitedherein. All methods described herein may be performed in any suitableorder unless otherwise indicated herein or otherwise clearlycontradicted by context. The use of any and all examples, or exemplarylanguage (e.g., “such as”) provided herein, is intended merely to betterilluminate the disclosure and does not pose a limitation on the scope ofthe disclosure unless otherwise claimed. No language in thespecification should be construed as indicating any non-claimed elementas essential to the practice of the disclosure.

While the foregoing written description enables one skilled in the artto make and use what is considered presently to be the best modethereof, those skilled in the art will understand and appreciate theexistence of variations, combinations, and equivalents of the specificembodiment, method, and examples herein. The disclosure should thereforenot be limited by the above-described embodiment, method, and examples,but by all embodiments and methods within the scope and spirit of thedisclosure.

Any element in a claim that does not explicitly state “means for”performing a specified function, or “step for” performing a specifiedfunction, is not to be interpreted as a “means” or “step” clause asspecified in 35 U.S.C. § 112(f). In particular, any use of “step of” inthe claims is not intended to invoke the provision of 35 U.S.C. §112(f).

Persons skilled in the art may appreciate that numerous designconfigurations may be possible to enjoy the functional benefits of theinventive systems. Thus, given the wide variety of configurations andarrangements of embodiments of the present invention the scope of theinvention is reflected by the breadth of the claims below rather thannarrowed by the embodiments described above.

1-271. (canceled)
 272. A method, comprising: recording biomarker dataover a time frame, the biomarker data being indicative of at least onebiological state of a user remaining in a lighting control environmentover the time frame, the biomarker data being generated by providing atleast one physiological sensor remaining with the user in the lightingcontrol environment over the time frame; recording light controlsettings data over the time frame for at least one light remaining inthe lighting control environment with the user and with the at least onephysiological sensor generating the biomarker data over the time frame;and using machine learning for recording data correlations over the timeframe between the at least one biological state of the user remaining inthe lighting control environment over the time frame and lightingeffects caused by the at least one light remaining in the lightingcontrol environment with the user over the time frame, the datacorrelations being based on the recordings of the biomarker data and therecordings of the light control settings data, and utilizing the datacorrelations for controlling the at least one light. 273-427. (canceled)428. The method of claim 272, wherein using the machine learningincludes using a nearest neighbor interpolation or a Kaczmarz method.429. The method of claim 272, wherein utilizing the data correlationsincludes adapting the light control settings data for the at least onelight in the lighting control environment based on the biomarker datagenerated by the at least one physiological sensor remaining with theuser in the lighting control environment.
 430. The method of claim 272,wherein utilizing the data correlations includes adapting the lightcontrol settings data for the at least one light in the lighting controlenvironment based on feedback on the lighting effects caused by the atleast one light remaining in the lighting control environment over thetime frame.
 431. The method of claim 272, wherein recording the datacorrelations includes classifying the lighting effects based on ameasurable effect on the user.
 432. The method of claim 272, whereinrecording the data correlations includes classifying the lightingeffects based on a measurable productivity effect or health effect onthe user.
 433. The method of claim 432, wherein classifying the lightingeffects includes storing the light control settings data as beingcorrelated with the lighting effects in a light fixture library. 434.The method of claim 272, wherein recording the light control settingsdata includes causing the at least one light to generate light varyingover the time frame through a range of color, intensity, spectrum,direction, shape, or distance.
 435. The method of claim 272, whereinproviding the at least one physiological sensor includes providing awearable sensor.
 436. The method of claim 272, wherein the biomarkerdata is generated by providing the at least one physiological sensor asincluding another physiological sensor.
 437. A non-transitory computerreadable medium having stored thereon processor-executable softwareinstructions that, when executed by a processor, cause the processor togenerate control signals for recording data correlations between atleast one biological state of a user and lighting effects caused by atleast one light in a lighting control environment, by executing thesteps comprising: recording biomarker data over a time frame, thebiomarker data being indicative of at least one biological state of auser remaining in a lighting control environment over the time frame,the biomarker data being generated by providing at least onephysiological sensor remaining with the user in the lighting controlenvironment over the time frame; recording light control settings dataover the time frame for at least one light remaining in the lightingcontrol environment with the user and with the at least onephysiological sensor generating the biomarker data over the time frame;and using machine learning for recording data correlations over the timeframe between the at least one biological state of the user remaining inthe lighting control environment over the time frame and lightingeffects caused by the at least one light remaining in the lightingcontrol environment with the user over the time frame, the datacorrelations being based on the recordings of the biomarker data and therecordings of the light control settings data, and utilizing the datacorrelations for controlling the at least one light.
 438. Thenon-transitory computer readable medium of claim 437, wherein using themachine learning includes using a nearest neighbor interpolation or aKaczmarz method.
 439. The non-transitory computer readable medium ofclaim 437, wherein utilizing the data correlations includes adapting thelight control settings data for the at least one light in the lightingcontrol environment based on the biomarker data generated by the atleast one physiological sensor remaining with the user in the lightingcontrol environment.
 440. The non-transitory computer readable medium ofclaim 437, wherein utilizing the data correlations includes adapting thelight control settings data for the at least one light in the lightingcontrol environment based on feedback on the lighting effects caused bythe at least one light remaining in the lighting control environmentover the time frame.
 441. The non-transitory computer readable medium ofclaim 437, wherein recording the data correlations includes classifyingthe lighting effects based on a measurable effect on the user.
 442. Thenon-transitory computer readable medium of claim 437, wherein recordingthe data correlations includes classifying the lighting effects based ona measurable productivity effect or health effect on the user.
 443. Thenon-transitory computer readable medium of claim 442, whereinclassifying the lighting effects includes storing the light controlsettings data as being correlated with the lighting effects in a lightfixture library.
 444. The non-transitory computer readable medium ofclaim 437, wherein recording the light control settings data includescausing the at least one light to generate light varying over the timeframe through a range of color, intensity, spectrum, direction, shape,or distance.
 445. The non-transitory computer readable medium of claim437, wherein providing the at least one physiological sensor includesproviding a wearable sensor.
 446. The non-transitory computer readablemedium of claim 437, wherein the biomarker data is generated byproviding the at least one physiological sensor as including anotherphysiological sensor.